Clean the environment.

Set locations, and the working directory …


Defining phenotypes and datasets.

Create a new analysis directory, including subdirectories.
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE

Setting working directory and listing its contents.
[1] "/Volumes/LaCie/PLINK/analyses/lookups/AE_TEMPLATE/scRNAseq"
[1] "20211029.AESCRNA.scrnaseq_results.RData" "AESCRNA"                                 "scRNAseq.nb.html"                        "scRNAseq.Rmd"                           

… a package-installation function …

… and load those packages.

We will create a datestamp and define the Utrecht Science Park Colour Scheme.

1 ERA-CVD ‘druggable-MI-targets’

For the ERA-CVD ‘druggable-MI-targets’ project (grantnumber: 01KL1802) we will perform two related RNA sequencing (RNAseq) experiments:

  1. conventional (‘bulk’) RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of Friday, October 29, 2021 all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

  2. single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of Friday, October 29, 2021 data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the Athero-Express Biobank Study which is an ongoing study in the UMC Utrecht.

2 Background

Collaboration to study gene expression of PCSK9 in relation to atherosclerotic plaques characteristics. The main list of genes are given below.

  • Genes.xlsx
library(openxlsx)

# Manual option
# gene_list <- c("PCSK9", "COL4A1", "COL4A2", "COL3A", "COL2A", "LDLR", "CD36")
# gene_list

gene_list <- read.xlsx(paste0(PROJECTROOT_loc, "/SNP/Genes.xlsx"), sheet = "Genes")

DT::datatable(gene_list)

target_genes <- unlist(gene_list$Gene)
target_genes
[1] "PCSK9"  "COL4A1" "COL4A2" "COL3A"  "COL2A"  "LDLR"   "CD36"  

3 Load data

First we will load the data:

  • scRNAseq experimental data and rename the cell types.
  • Athero-Express clinical data.

3.1 AESCRNA: single-cell RNAseq from carotid plaques

Here we load the latest dataset from our Athero-Express Single Cell RNA experiment.

There are few datasets available:

  • 20210316_CircRes2020_18pts.RDS > the data associated with Depuydt M.A.C et al.
  • 20200701_seurat_37_pts.RDS > the data of 37 patients
  • 20210217_PlaqView_38_pts.RDS > the data associated with PlaqView; this can not be couple to study numbers
  • 20210811_46_patients_Koen.RDS > the latest dataset - NOTE: failes to open ‘unknown input format’

Here we use the PlaqView data.


scRNAseqData <- readRDS(paste0(RAWDATA, "/Seuset_40_patients/seurat_37_pts_20200701.RDS"))
scRNAseqData
An object of class Seurat 
38835 features across 6191 samples within 2 assays 
Active assay: SCT (18283 features, 3000 variable features)
 1 other assay present: RNA
 2 dimensional reductions calculated: pca, umap
N_GENES=18283

The naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as ‘KIT+ Mast cells”-like cells. Likewise we refer to the cell clusters as ’communities’ of cells that exihibit similar properties, i.e. similar defining markers (e.g. KIT).

We will rename the cell types to human readable names.

### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")


levels(unique(scRNAseqData@active.ident))
 [1] "CD3+CD8A+ T cells I"         "CD3+CD8A+ T cells III"       "CD3+CD4+ T Cells I"          "CD14+CD68+ Macrophages I"    "Mixed Cells I"               "CD3+CD8A+ T Cells II"       
 [7] "CD14+CD68+ Macrophages II"   "CD3+CD4+ T Cells II"         "ACTA2+ Smooth Muscle Cells"  "CD34+ Endothelial Cells I"   "CD34+ Endothelial Cells II"  "NCAM1+ Natural Killer Cells"
[13] "Mixed Cells II"              "CD79A+ B Cells I"            "CD14+CD68+ Macrophages III"  "CD3+ Regulatory T Cells"     "KIT+ Mast Cells"             "CD79A+ B Cells II"          
# [1] "CD3+CD8A+ T cells I"         "CD3+CD8A+ T cells III"       "CD3+CD4+ T Cells I"          "CD14+CD68+ Macrophages I"   
# [5] "Mixed Cells I"               "CD3+CD8A+ T Cells II"        "CD14+CD68+ Macrophages II"   "CD3+CD4+ T Cells II"        
# [9] "ACTA2+ Smooth Muscle Cells"  "CD34+ Endothelial Cells I"   "CD34+ Endothelial Cells II"  "NCAM1+ Natural Killer Cells"
#[13] "Mixed Cells II"              "CD79A+ B Cells I"            "CD14+CD68+ Macrophages III"  "CD3+ Regulatory T Cells"    
#[17] "KIT+ Mast Cells"             "CD79A+ B Cells II"  

celltypes <- c("CD14+CD68+ Macrophages I" = "CD14+CD68+ M I", 
               "CD14+CD68+ Macrophages II" = "CD14+CD68+ M II", 
               "CD14+CD68+ Macrophages III" = "CD14+CD68+ M III",
               "CD3+CD8A+ T cells I" = "CD3+CD8+ T I",
               "CD3+CD8A+ T Cells II" = "CD3+CD8A+ T II",
               "CD3+CD8A+ T cells III" = "CD3+CD8A+ T III",
               "CD3+CD4+ T Cells I" = "CD3+CD4+ T I", 
               "CD3+CD4+ T Cells II" = "CD3+CD4+ T II", 
               "CD3+ Regulatory T Cells" = "CD3 Tregs", 
               "CD34+ Endothelial Cells I" = "CD34+ EC I", 
               "CD34+ Endothelial Cells II" = "CD34+ EC II", 
               "Mixed Cells I" = "Mixed I", 
               "Mixed Cells II" = "Mixed II", 
               "ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC", 
               "NCAM1+ Natural Killer Cells" = "NCAM1+ NK", 
               "KIT+ Mast Cells" = "KIT+ MC",
               "CD79A+ B Cells I" = "CD79A+ B I", 
               "CD79A+ B Cells II" = "CD79A+ B II")

scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData, 
                                       celltypes)
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

3.2 Clinical data

Loading Athero-Express clinical data.

require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2021_1_NEW_AtheroExpressDatabase_ScientificAE_01-02-2021.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2021_3_NEW_AtheroExpressDatabase_ScientificAE_10-09-2021.sav"))

3.2.1 Fixing and creating variables

We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.

Coding of symptoms is as follows:

  • missing -999
  • Asymptomatic 0
  • TIA 1
  • minor stroke 2
  • Major stroke 3
  • Amaurosis fugax 4
  • Four vessel disease 5
  • Vertebrobasilary TIA 7
  • Retinal infarction 8
  • Symptomatic, but aspecific symtoms 9
  • Contralateral symptomatic occlusion 10
  • retinal infarction 11
  • armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
  • retinal infarction + TIAs 13
  • Ocular ischemic syndrome 14
  • ischemisch glaucoom 15
  • subclavian steal syndrome 16
  • TGA 17

We will group as follows in Symptoms.5G:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13
  3. Stroke > 2, 3
  4. Ocular > 4, 14, 15
  5. Retinal infarction > 8, 11
  6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in AsymptSympt:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13 + Stroke > 2, 3
  3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in AsymptSympt2G:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
              
               Asymptomatic Ocular Other Retinal infarction Stroke  TIA <NA>
  Asymptomatic          346      0     0                  0      0    0    0
  Symptomatic             0    447   121                 49    761 1070    0
  <NA>                    0      0     0                  0      0    0 1130
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

We will also fix the plaquephenotypes variable.

Coding of symptoms is as follows:

  • missing -999
  • not relevant -888
  • fibrous 1
  • fibroatheromatous 2
  • atheromatous 3

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

     atheromatous fibroatheromatous           fibrous 
              593               879              1482 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the diabetes status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

   0    1 
2882 1033 
table(AEDB$DiabetesStatus)

Control (no Diabetes Dx/Med)                     Diabetes 
                        2882                         1033 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.

  • diet801: are you a smoker?
  • diet802: did you smoke in the past?

We already have some variables indicating smoking status:

  • SmokingReported: patient has reported to smoke.
  • SmokingYearOR: smoking in the year of surgery?
  • SmokerCurrent: currently smoking?
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")

Fixing smoking status.
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")

* Current smoking status.
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))
Current smoker
no data available/missing                        no                       yes                      <NA> 
                        0                      2472                      1377                        75 
cat("\n* Updated smoking status.\n")

* Updated smoking status.
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))
Updated smoking status
Current smoker      Ex-smoker   Never smoked           <NA> 
          1377           1893            416            238 
cat("\n* Comparing to 'SmokerCurrent'.\n")

* Comparing to 'SmokerCurrent'.
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))
                      Current smoker
Updated smoking status no data available/missing   no  yes <NA>
        Current smoker                         0    0 1377    0
        Ex-smoker                              0 1893    0    0
        Never smoked                           0  416    0    0
        <NA>                                   0  163    0   75
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the alcohol status variable.


# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

  No  Yes 
1295 2440 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv


# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)

   0    1 
2555 1353 
table(AEDB$Stroke_history)

   0    1 
2858  982 
table(AEDB$Peripheral.interv)

   0    1 
2709 1186 
table(AEDB$MedHx_CVD)

  No  yes 
1305 2609 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix and inverse-rank normal transform the continuous (manually) scored plaque phenotypes.

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

AEDB$MAC_rankNorm <- qnorm((rank(AEDB$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$macmean0)))
AEDB$SMC_rankNorm <- qnorm((rank(AEDB$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$smcmean0)))
AEDB$Neutrophils_rankNorm <- qnorm((rank(AEDB$neutrophils, na.last = "keep") - 0.5) / sum(!is.na(AEDB$neutrophils)))
AEDB$MastCells_rankNorm <- qnorm((rank(AEDB$Mast_cells_plaque, na.last = "keep") - 0.5) / sum(!is.na(AEDB$Mast_cells_plaque)))
AEDB$VesselDensity_rankNorm <- qnorm((rank(AEDB$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB$vessel_density_averaged)))
library(labelled)
AEDB$Gender <- to_factor(AEDB$Gender)
ggpubr::gghistogram(AEDB, "macmean0", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% of macrophages (CD68)",
                    xlab = "% per region of interest", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 1361 rows containing non-finite values (stat_bin).

ggpubr::gghistogram(AEDB, "MAC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% of macrophages (CD68)",
                   xlab = "% per region of interest\ninverse-rank normalized number", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 1361 rows containing non-finite values (stat_bin).

ggpubr::gghistogram(AEDB, "smcmean0", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% of smooth muscle cells (SMA)",
                    xlab = "% per region of interest", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 1363 rows containing non-finite values (stat_bin).

ggpubr::gghistogram(AEDB, "SMC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% of smooth muscle cells (SMA)",
                   xlab = "% per region of interest\ninverse-rank normalized number", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 1363 rows containing non-finite values (stat_bin).

ggpubr::gghistogram(AEDB, "neutrophils", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of neutrophils (CD66b)",
                    xlab = "counts per plaque", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 3583 rows containing non-finite values (stat_bin).

ggpubr::gghistogram(AEDB, "Neutrophils_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of neutrophils (CD66b)",
                   xlab = "counts per plaque\ninverse-rank normalized number", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 3583 rows containing non-finite values (stat_bin).

ggpubr::gghistogram(AEDB, "Mast_cells_plaque", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of mast cells",
                    xlab = "counts per plaque", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 3660 rows containing non-finite values (stat_bin).

ggpubr::gghistogram(AEDB, "MastCells_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of mast cells",
                   xlab = "counts per plaque\ninverse-rank normalized number", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 3660 rows containing non-finite values (stat_bin).

ggpubr::gghistogram(AEDB, "vessel_density_averaged", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels",
                    xlab = "counts per 3-4 hotspots", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 1954 rows containing non-finite values (stat_bin).

ggpubr::gghistogram(AEDB, "VesselDensity_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels",
                   xlab = "counts per 3-4 hotspots\ninverse-rank normalized number", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 1954 rows containing non-finite values (stat_bin).

Here we calculate the plaque instability/vulnerability index

# Plaque vulnerability
require(labelled)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)

table(AEDB$Macrophages.bin)

      no/minor moderate/heavy 
          1607           1220 
table(AEDB$Fat.bin_10)

 <10%  >10% 
 1270  1708 
table(AEDB$Collagen.bin)

      no/minor moderate/heavy 
           541           2306 
table(AEDB$SMC.bin)

      no/minor moderate/heavy 
           874           1968 
table(AEDB$IPH.bin)

  no  yes 
1223 1628 
# SPSS code

# 
# *** syntax- Plaque vulnerability**.
# COMPUTE Macro_instab = -999.
# IF macrophages.bin=2 Macro_instab=1.
# IF macrophages.bin=1 Macro_instab=0.
# EXECUTE.
# 
# COMPUTE Fat10_instab = -999.
# IF Fat.bin_10=2 Fat10_instab=1.
# IF Fat.bin_10=1 Fat10_instab=0.
# EXECUTE.
# 
# COMPUTE coll_instab=-999.
# IF Collagen.bin=2 coll_instab=0.
# IF Collagen.bin=1 coll_instab=1.
# EXECUTE.
# 
# 
# COMPUTE SMC_instab=-999.
# IF SMC.bin=2 SMC_instab=0.
# IF SMC.bin=1 SMC_instab=1.
# EXECUTE.
# 
# COMPUTE IPH_instab=-999.
# IF IPH.bin=0 IPH_instab=0.
# IF IPH.bin=1 IPH_instab=1.
# EXECUTE.
# 
# COMPUTE Instability=Macro_instab + Fat10_instab +  coll_instab + SMC_instab + IPH_instab.
# EXECUTE.

# Fix plaquephenotypes
attach(AEDB)
# mac instability
AEDB[,"MAC_Instability"] <- NA
AEDB$MAC_Instability[Macrophages.bin == -999] <- NA
AEDB$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
AEDB$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1

# fat instability
AEDB[,"FAT10_Instability"] <- NA
AEDB$FAT10_Instability[Fat.bin_10 == -999] <- NA
AEDB$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
AEDB$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1

# col instability 
AEDB[,"COL_Instability"] <- NA
AEDB$COL_Instability[Collagen.bin == -999] <- NA
AEDB$COL_Instability[Collagen.bin == "no/minor"] <- 1
AEDB$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0

# smc instability
AEDB[,"SMC_Instability"] <- NA
AEDB$SMC_Instability[SMC.bin == -999] <- NA
AEDB$SMC_Instability[SMC.bin == "no/minor"] <- 1
AEDB$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0

# iph instability
AEDB[,"IPH_Instability"] <- NA
AEDB$IPH_Instability[IPH.bin == -999] <- NA
AEDB$IPH_Instability[IPH.bin == "no"] <- 0
AEDB$IPH_Instability[IPH.bin == "yes"] <- 1

detach(AEDB)

table(AEDB$MAC_Instability, useNA = "ifany")

   0    1 <NA> 
1607 1220 1097 
table(AEDB$FAT10_Instability, useNA = "ifany")

   0    1 <NA> 
1270 1708  946 
table(AEDB$COL_Instability, useNA = "ifany")

   0    1 <NA> 
2306  541 1077 
table(AEDB$SMC_Instability, useNA = "ifany")

   0    1 <NA> 
1968  874 1082 
table(AEDB$IPH_Instability, useNA = "ifany")

   0    1 <NA> 
1223 1628 1073 
# creating vulnerability index
AEDB <- AEDB %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
                                )
mutate: new variable 'Plaque_Vulnerability_Index' (factor) with 6 unique values and 0% NA
table(AEDB$Plaque_Vulnerability_Index, useNA = "ifany")

   0    1    2    3    4    5 
1375  732  730  679  298  110 
# str(AEDB$Plaque_Vulnerability_Index)

3.3 Athero-Express Biobank Study

3.3.1 Prepare baseline summary

We are interested in the following variables at baseline.

  • Age (years)

  • Female sex (N, %)

  • Hypertension (N, %)

  • SBP (mmHg)

  • DBP (mmHg)

  • Diabetes mellitus (N, %)

  • Total cholesterol levels (mg/dL)

  • LDL cholesterol levels (mg/dL)

  • HDL cholesterol levels (mg/dL)

  • Triglyceride levels (mg/dL)

  • Use of statins (N, %)

  • Use of antiplatelet drugs (N, %)

  • BMI (kg/m²)

  • Smoking status (N, %)

    • Never smokers
    • Ex-smokers
    • Current smokers
  • History of CAD (N, %)

  • History of PAD (N, %)

  • Clinical manifestations

    • Asymptomatic
    • Amaurosis fugax
    • TIA
    • Stroke
  • eGFR (mL/min/1.73 m²)

  • stenosis

  • year of surgery

  • plaque characteristics

cat("===========================================================================================\n")
===========================================================================================
cat("CREATE BASELINE TABLE\n")
CREATE BASELINE TABLE
# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", 
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "SMC_rankNorm", "MAC_rankNorm", "Neutrophils_rankNorm", "MastCells_rankNorm", "VesselDensity_rankNorm")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", 
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con

3.3.2 All patients

Showing the baseline table of the whole Athero-Express Biobank.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.full, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
                                      
                                       level                                                                     Overall           Missing
  n                                                                                                                 3587                  
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     43.7 (1567)     0.0   
                                       UMC Utrecht                                                                  56.3 (2020)           
  ORyear % (freq)                      No data available/missing                                                     0.0 (   0)     0.0   
                                       2002                                                                          2.4 (  86)           
                                       2003                                                                          5.3 ( 191)           
                                       2004                                                                          7.4 ( 265)           
                                       2005                                                                          7.7 ( 276)           
                                       2006                                                                          7.1 ( 254)           
                                       2007                                                                          5.7 ( 206)           
                                       2008                                                                          5.3 ( 190)           
                                       2009                                                                          6.9 ( 246)           
                                       2010                                                                          7.8 ( 278)           
                                       2011                                                                          6.8 ( 243)           
                                       2012                                                                          7.9 ( 284)           
                                       2013                                                                          6.6 ( 235)           
                                       2014                                                                          7.8 ( 281)           
                                       2015                                                                          2.1 (  77)           
                                       2016                                                                          3.4 ( 121)           
                                       2017                                                                          2.3 (  81)           
                                       2018                                                                          2.3 (  82)           
                                       2019                                                                          2.4 (  85)           
                                       2020                                                                          1.9 (  68)           
                                       2021                                                                          1.1 (  38)           
  Age (mean (SD))                                                                                                 68.796 (9.197)    0.0   
  Gender % (freq)                      female                                                                       29.7 (1067)     0.0   
                                       male                                                                         70.3 (2520)           
  TC_finalCU (mean (SD))                                                                                         186.441 (105.068) 45.8   
  LDL_finalCU (mean (SD))                                                                                        106.593 (45.978)  53.2   
  HDL_finalCU (mean (SD))                                                                                         46.639 (16.721)  50.0   
  TG_finalCU (mean (SD))                                                                                         154.634 (98.415)  50.8   
  TC_final (mean (SD))                                                                                             4.829 (2.721)   45.8   
  LDL_final (mean (SD))                                                                                            2.761 (1.191)   53.2   
  HDL_final (mean (SD))                                                                                            1.208 (0.433)   50.0   
  TG_final (mean (SD))                                                                                             1.747 (1.112)   50.8   
  systolic (mean (SD))                                                                                           150.433 (24.795)  12.7   
  diastoli (mean (SD))                                                                                            79.894 (22.126)  12.7   
  GFR_MDRD (mean (SD))                                                                                            75.175 (24.580)   5.8   
  BMI (mean (SD))                                                                                                 26.433 (4.113)    3.7   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (   0)     5.8   
                                       Normal kidney function                                                       22.4 ( 803)           
                                       CKD 2 (Mild)                                                                 48.0 (1721)           
                                       CKD 3 (Moderate)                                                             21.7 ( 779)           
                                       CKD 4 (Severe)                                                                1.5 (  53)           
                                       CKD 5 (Failure)                                                               0.6 (  22)           
                                       <NA>                                                                          5.8 ( 209)           
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (   0)     3.7   
                                       Underweight                                                                   1.1 (  40)           
                                       Normal                                                                       36.1 (1296)           
                                       Overweight                                                                   44.2 (1584)           
                                       Obese                                                                        14.9 ( 533)           
                                       <NA>                                                                          3.7 ( 134)           
  SmokerStatus % (freq)                Current smoker                                                               34.9 (1252)     4.3   
                                       Ex-smoker                                                                    50.1 (1796)           
                                       Never smoked                                                                 10.7 ( 384)           
                                       <NA>                                                                          4.3 ( 155)           
  AlcoholUse % (freq)                  No                                                                           32.6 (1171)     3.4   
                                       Yes                                                                          64.0 (2294)           
                                       <NA>                                                                          3.4 ( 122)           
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 73.9 (2651)     0.2   
                                       Diabetes                                                                     25.9 ( 928)           
                                       <NA>                                                                          0.2 (   8)           
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (   0)     2.6   
                                       no                                                                           23.8 ( 854)           
                                       yes                                                                          73.6 (2640)           
                                       <NA>                                                                          2.6 (  93)           
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (   0)     3.8   
                                       no                                                                           29.0 (1039)           
                                       yes                                                                          67.3 (2413)           
                                       <NA>                                                                          3.8 ( 135)           
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (   0)     0.3   
                                       no                                                                           13.7 ( 493)           
                                       yes                                                                          85.9 (3082)           
                                       <NA>                                                                          0.3 (  12)           
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (   0)     0.6   
                                       no                                                                           21.2 ( 759)           
                                       yes                                                                          78.2 (2805)           
                                       <NA>                                                                          0.6 (  23)           
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (   0)     1.0   
                                       no                                                                           86.4 (3098)           
                                       yes                                                                          12.7 ( 454)           
                                       <NA>                                                                          1.0 (  35)           
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (   0)     0.8   
                                       no                                                                           13.4 ( 482)           
                                       yes                                                                          85.8 (3078)           
                                       <NA>                                                                          0.8 (  27)           
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (   0)     0.7   
                                       no                                                                           20.9 ( 748)           
                                       yes                                                                          78.5 (2815)           
                                       <NA>                                                                          0.7 (  24)           
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (   0)     5.3   
                                       No stroke diagnosed                                                          77.3 (2771)           
                                       Stroke diagnosed                                                             17.5 ( 626)           
                                       <NA>                                                                          5.3 ( 190)           
  sympt % (freq)                       missing                                                                      28.1 (1008)     0.0   
                                       Asymptomatic                                                                  9.1 ( 327)           
                                       TIA                                                                          27.7 ( 992)           
                                       minor stroke                                                                 11.4 ( 408)           
                                       Major stroke                                                                  7.3 ( 263)           
                                       Amaurosis fugax                                                              11.2 ( 401)           
                                       Four vessel disease                                                           1.1 (  39)           
                                       Vertebrobasilary TIA                                                          0.1 (   5)           
                                       Retinal infarction                                                            1.0 (  37)           
                                       Symptomatic, but aspecific symtoms                                            1.6 (  59)           
                                       Contralateral symptomatic occlusion                                           0.3 (  11)           
                                       retinal infarction                                                            0.3 (   9)           
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (   1)           
                                       retinal infarction + TIAs                                                     0.0 (   0)           
                                       Ocular ischemic syndrome                                                      0.7 (  25)           
                                       ischemisch glaucoom                                                           0.0 (   0)           
                                       subclavian steal syndrome                                                     0.1 (   2)           
                                       TGA                                                                           0.0 (   0)           
  Symptoms.5G % (freq)                 Asymptomatic                                                                  9.1 ( 327)    28.1   
                                       Ocular                                                                       11.9 ( 426)           
                                       Other                                                                         3.1 ( 112)           
                                       Retinal infarction                                                            1.3 (  46)           
                                       Stroke                                                                       18.7 ( 671)           
                                       TIA                                                                          27.8 ( 997)           
                                       <NA>                                                                         28.1 (1008)           
  AsymptSympt % (freq)                 Asymptomatic                                                                  9.1 ( 327)    28.1   
                                       Ocular and others                                                            16.3 ( 584)           
                                       Symptomatic                                                                  46.5 (1668)           
                                       <NA>                                                                         28.1 (1008)           
  restenos % (freq)                    missing                                                                       0.0 (   0)     3.8   
                                       de novo                                                                      86.9 (3117)           
                                       restenosis                                                                    9.1 ( 328)           
                                       stenose bij angioseal na PTCA                                                 0.1 (   5)           
                                       <NA>                                                                          3.8 ( 137)           
  stenose % (freq)                     missing                                                                       0.0 (   0)     6.5   
                                       0-49%                                                                         0.7 (  24)           
                                       50-70%                                                                        7.1 ( 255)           
                                       70-90%                                                                       36.0 (1290)           
                                       90-99%                                                                       30.1 (1079)           
                                       100% (Occlusion)                                                             14.1 ( 504)           
                                       NA                                                                            0.1 (   3)           
                                       50-99%                                                                        2.5 (  89)           
                                       70-99%                                                                        3.0 ( 107)           
                                       99                                                                            0.1 (   2)           
                                       <NA>                                                                          6.5 ( 234)           
  MedHx_CVD % (freq)                   No                                                                           33.5 (1202)     0.3   
                                       yes                                                                          66.2 (2376)           
                                       <NA>                                                                          0.3 (   9)           
  CAD_history % (freq)                 Missing                                                                       0.0 (   0)     0.3   
                                       No history CAD                                                               65.2 (2338)           
                                       History CAD                                                                  34.5 (1237)           
                                       <NA>                                                                          0.3 (  12)           
  PAOD % (freq)                        missing/no data                                                               0.0 (   0)     0.3   
                                       no                                                                           56.6 (2031)           
                                       yes                                                                          43.1 (1547)           
                                       <NA>                                                                          0.3 (   9)           
  Peripheral.interv % (freq)           no                                                                           69.0 (2476)     0.6   
                                       yes                                                                          30.4 (1090)           
                                       <NA>                                                                          0.6 (  21)           
  EP_composite % (freq)                No data available.                                                            0.0 (   0)     5.2   
                                       No composite endpoints                                                       62.9 (2258)           
                                       Composite endpoints                                                          31.9 (1143)           
                                       <NA>                                                                          5.2 ( 186)           
  EP_composite_time (mean (SD))                                                                                    2.340 (1.181)    5.5   
  macmean0 (mean (SD))                                                                                             0.659 (1.159)   35.3   
  smcmean0 (mean (SD))                                                                                             2.185 (2.698)   35.3   
  Macrophages.bin % (freq)             no/minor                                                                     40.1 (1440)    28.8   
                                       moderate/heavy                                                               31.1 (1114)           
                                       <NA>                                                                         28.8 (1033)           
  SMC.bin % (freq)                     no/minor                                                                     22.3 ( 801)    28.4   
                                       moderate/heavy                                                               49.2 (1766)           
                                       <NA>                                                                         28.4 (1020)           
  neutrophils (mean (SD))                                                                                        144.084 (415.728) 91.3   
  Mast_cells_plaque (mean (SD))                                                                                  164.399 (163.438) 93.2   
  IPH.bin % (freq)                     no                                                                           30.6 (1099)    28.0   
                                       yes                                                                          41.3 (1483)           
                                       <NA>                                                                         28.0 (1005)           
  vessel_density_averaged (mean (SD))                                                                              8.075 (6.275)   50.2   
  Calc.bin % (freq)                    no/minor                                                                     38.6 (1384)    24.6   
                                       moderate/heavy                                                               36.8 (1319)           
                                       <NA>                                                                         24.6 ( 884)           
  Collagen.bin % (freq)                no/minor                                                                     13.9 ( 497)    28.3   
                                       moderate/heavy                                                               57.8 (2075)           
                                       <NA>                                                                         28.3 (1015)           
  Fat.bin_10 % (freq)                   <10%                                                                        31.7 (1136)    24.6   
                                        >10%                                                                        43.7 (1567)           
                                       <NA>                                                                         24.6 ( 884)           
  Fat.bin_40 % (freq)                  <40%                                                                         59.4 (2132)    24.6   
                                       >40%                                                                         15.9 ( 571)           
                                       <NA>                                                                         24.6 ( 884)           
  OverallPlaquePhenotype % (freq)      atheromatous                                                                 15.3 ( 550)    25.3   
                                       fibroatheromatous                                                            22.5 ( 806)           
                                       fibrous                                                                      36.9 (1324)           
                                       <NA>                                                                         25.3 ( 907)           
  SMC_rankNorm (mean (SD))                                                                                        -0.003 (1.004)   35.3   
  MAC_rankNorm (mean (SD))                                                                                         0.004 (0.993)   35.3   
  Neutrophils_rankNorm (mean (SD))                                                                                 0.010 (0.953)   91.3   
  MastCells_rankNorm (mean (SD))                                                                                  -0.009 (1.000)   93.2   
  VesselDensity_rankNorm (mean (SD))                                                                               0.017 (0.978)   50.2   

3.3.3 CEA patients

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
                                      
                                       level                                                                     Overall           Missing
  n                                                                                                                 2533                  
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     37.5 ( 951)     0.0   
                                       UMC Utrecht                                                                  62.5 (1582)           
  ORyear % (freq)                      No data available/missing                                                     0.0 (   0)     0.0   
                                       2002                                                                          3.2 (  81)           
                                       2003                                                                          6.2 ( 157)           
                                       2004                                                                          7.5 ( 190)           
                                       2005                                                                          7.3 ( 186)           
                                       2006                                                                          7.2 ( 183)           
                                       2007                                                                          6.0 ( 152)           
                                       2008                                                                          5.5 ( 139)           
                                       2009                                                                          7.2 ( 183)           
                                       2010                                                                          6.3 ( 159)           
                                       2011                                                                          6.5 ( 165)           
                                       2012                                                                          7.1 ( 179)           
                                       2013                                                                          5.9 ( 150)           
                                       2014                                                                          6.5 ( 164)           
                                       2015                                                                          3.0 (  76)           
                                       2016                                                                          3.4 (  85)           
                                       2017                                                                          2.6 (  66)           
                                       2018                                                                          2.7 (  69)           
                                       2019                                                                          2.6 (  65)           
                                       2020                                                                          2.2 (  55)           
                                       2021                                                                          1.1 (  29)           
  Age (mean (SD))                                                                                                 69.167 (9.287)    0.0   
  Gender % (freq)                      female                                                                       30.6 ( 774)     0.0   
                                       male                                                                         69.4 (1759)           
  TC_finalCU (mean (SD))                                                                                         184.109 (55.942)  38.1   
  LDL_finalCU (mean (SD))                                                                                        107.710 (41.683)  45.4   
  HDL_finalCU (mean (SD))                                                                                         46.464 (16.855)  41.7   
  TG_finalCU (mean (SD))                                                                                         151.857 (91.217)  42.7   
  TC_final (mean (SD))                                                                                             4.768 (1.449)   38.1   
  LDL_final (mean (SD))                                                                                            2.790 (1.080)   45.4   
  HDL_final (mean (SD))                                                                                            1.203 (0.437)   41.7   
  TG_final (mean (SD))                                                                                             1.716 (1.031)   42.7   
  systolic (mean (SD))                                                                                           151.930 (24.994)  11.3   
  diastoli (mean (SD))                                                                                            81.065 (24.738)  11.3   
  GFR_MDRD (mean (SD))                                                                                            73.514 (21.454)   4.1   
  BMI (mean (SD))                                                                                                 26.549 (4.110)    3.8   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (   0)     4.2   
                                       Normal kidney function                                                       19.9 ( 505)           
                                       CKD 2 (Mild)                                                                 51.3 (1299)           
                                       CKD 3 (Moderate)                                                             22.9 ( 579)           
                                       CKD 4 (Severe)                                                                1.3 (  34)           
                                       CKD 5 (Failure)                                                               0.4 (  10)           
                                       <NA>                                                                          4.2 ( 106)           
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (   0)     3.9   
                                       Underweight                                                                   1.0 (  25)           
                                       Normal                                                                       35.7 ( 905)           
                                       Overweight                                                                   44.0 (1114)           
                                       Obese                                                                        15.4 ( 390)           
                                       <NA>                                                                          3.9 (  99)           
  SmokerStatus % (freq)                Current smoker                                                               33.8 ( 855)     4.6   
                                       Ex-smoker                                                                    48.3 (1224)           
                                       Never smoked                                                                 13.3 ( 338)           
                                       <NA>                                                                          4.6 ( 116)           
  AlcoholUse % (freq)                  No                                                                           34.9 ( 883)     3.6   
                                       Yes                                                                          61.5 (1559)           
                                       <NA>                                                                          3.6 (  91)           
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 76.0 (1925)     0.2   
                                       Diabetes                                                                     23.8 ( 602)           
                                       <NA>                                                                          0.2 (   6)           
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (   0)     2.4   
                                       no                                                                           24.4 ( 618)           
                                       yes                                                                          73.2 (1854)           
                                       <NA>                                                                          2.4 (  61)           
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (   0)     3.4   
                                       no                                                                           29.9 ( 758)           
                                       yes                                                                          66.7 (1689)           
                                       <NA>                                                                          3.4 (  86)           
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (   0)     0.3   
                                       no                                                                           15.0 ( 380)           
                                       yes                                                                          84.7 (2146)           
                                       <NA>                                                                          0.3 (   7)           
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (   0)     0.6   
                                       no                                                                           23.6 ( 598)           
                                       yes                                                                          75.8 (1919)           
                                       <NA>                                                                          0.6 (  16)           
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (   0)     1.0   
                                       no                                                                           87.7 (2221)           
                                       yes                                                                          11.3 ( 286)           
                                       <NA>                                                                          1.0 (  26)           
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (   0)     0.8   
                                       no                                                                           12.4 ( 314)           
                                       yes                                                                          86.9 (2200)           
                                       <NA>                                                                          0.8 (  19)           
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (   0)     0.7   
                                       no                                                                           20.0 ( 507)           
                                       yes                                                                          79.3 (2009)           
                                       <NA>                                                                          0.7 (  17)           
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (   0)     4.5   
                                       No stroke diagnosed                                                          73.1 (1851)           
                                       Stroke diagnosed                                                             22.5 ( 569)           
                                       <NA>                                                                          4.5 ( 113)           
  sympt % (freq)                       missing                                                                       0.7 (  17)     0.0   
                                       Asymptomatic                                                                 11.0 ( 279)           
                                       TIA                                                                          38.8 ( 984)           
                                       minor stroke                                                                 16.1 ( 407)           
                                       Major stroke                                                                 10.4 ( 263)           
                                       Amaurosis fugax                                                              15.8 ( 400)           
                                       Four vessel disease                                                           1.5 (  38)           
                                       Vertebrobasilary TIA                                                          0.2 (   5)           
                                       Retinal infarction                                                            1.5 (  37)           
                                       Symptomatic, but aspecific symtoms                                            2.2 (  55)           
                                       Contralateral symptomatic occlusion                                           0.4 (  11)           
                                       retinal infarction                                                            0.4 (   9)           
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (   1)           
                                       retinal infarction + TIAs                                                     0.0 (   0)           
                                       Ocular ischemic syndrome                                                      1.0 (  25)           
                                       ischemisch glaucoom                                                           0.0 (   0)           
                                       subclavian steal syndrome                                                     0.1 (   2)           
                                       TGA                                                                           0.0 (   0)           
  Symptoms.5G % (freq)                 Asymptomatic                                                                 11.0 ( 279)     0.7   
                                       Ocular                                                                       16.8 ( 425)           
                                       Other                                                                         4.2 ( 107)           
                                       Retinal infarction                                                            1.8 (  46)           
                                       Stroke                                                                       26.5 ( 670)           
                                       TIA                                                                          39.0 ( 989)           
                                       <NA>                                                                          0.7 (  17)           
  AsymptSympt % (freq)                 Asymptomatic                                                                 11.0 ( 279)     0.7   
                                       Ocular and others                                                            22.8 ( 578)           
                                       Symptomatic                                                                  65.5 (1659)           
                                       <NA>                                                                          0.7 (  17)           
  restenos % (freq)                    missing                                                                       0.0 (   0)     1.6   
                                       de novo                                                                      93.5 (2369)           
                                       restenosis                                                                    4.9 ( 124)           
                                       stenose bij angioseal na PTCA                                                 0.0 (   0)           
                                       <NA>                                                                          1.6 (  40)           
  stenose % (freq)                     missing                                                                       0.0 (   0)     2.2   
                                       0-49%                                                                         0.6 (  14)           
                                       50-70%                                                                        7.9 ( 200)           
                                       70-90%                                                                       46.0 (1165)           
                                       90-99%                                                                       38.1 ( 965)           
                                       100% (Occlusion)                                                              1.3 (  34)           
                                       NA                                                                            0.0 (   1)           
                                       50-99%                                                                        0.7 (  17)           
                                       70-99%                                                                        3.2 (  80)           
                                       99                                                                            0.1 (   2)           
                                       <NA>                                                                          2.2 (  55)           
  MedHx_CVD % (freq)                   No                                                                           35.8 ( 908)     0.2   
                                       yes                                                                          64.0 (1620)           
                                       <NA>                                                                          0.2 (   5)           
  CAD_history % (freq)                 Missing                                                                       0.0 (   0)     0.3   
                                       No history CAD                                                               67.9 (1719)           
                                       History CAD                                                                  31.8 ( 806)           
                                       <NA>                                                                          0.3 (   8)           
  PAOD % (freq)                        missing/no data                                                               0.0 (   0)     0.3   
                                       no                                                                           78.6 (1992)           
                                       yes                                                                          21.1 ( 534)           
                                       <NA>                                                                          0.3 (   7)           
  Peripheral.interv % (freq)           no                                                                           78.4 (1986)     0.6   
                                       yes                                                                          21.0 ( 531)           
                                       <NA>                                                                          0.6 (  16)           
  EP_composite % (freq)                No data available.                                                            0.0 (   0)     5.6   
                                       No composite endpoints                                                       70.1 (1775)           
                                       Composite endpoints                                                          24.4 ( 617)           
                                       <NA>                                                                          5.6 ( 141)           
  EP_composite_time (mean (SD))                                                                                    2.490 (1.117)    5.8   
  macmean0 (mean (SD))                                                                                             0.769 (1.186)   32.5   
  smcmean0 (mean (SD))                                                                                             1.982 (2.378)   32.7   
  Macrophages.bin % (freq)             no/minor                                                                     33.7 ( 853)    27.0   
                                       moderate/heavy                                                               39.4 ( 997)           
                                       <NA>                                                                         27.0 ( 683)           
  SMC.bin % (freq)                     no/minor                                                                     24.0 ( 607)    26.7   
                                       moderate/heavy                                                               49.3 (1250)           
                                       <NA>                                                                         26.7 ( 676)           
  neutrophils (mean (SD))                                                                                        146.685 (419.386) 88.0   
  Mast_cells_plaque (mean (SD))                                                                                  164.488 (163.771) 90.4   
  IPH.bin % (freq)                     no                                                                           29.6 ( 749)    26.6   
                                       yes                                                                          43.9 (1111)           
                                       <NA>                                                                         26.6 ( 673)           
  vessel_density_averaged (mean (SD))                                                                              8.322 (6.386)   37.8   
  Calc.bin % (freq)                    no/minor                                                                     41.3 (1045)    24.8   
                                       moderate/heavy                                                               34.0 ( 860)           
                                       <NA>                                                                         24.8 ( 628)           
  Collagen.bin % (freq)                no/minor                                                                     15.2 ( 386)    26.5   
                                       moderate/heavy                                                               58.3 (1476)           
                                       <NA>                                                                         26.5 ( 671)           
  Fat.bin_10 % (freq)                   <10%                                                                        21.9 ( 555)    24.8   
                                        >10%                                                                        53.3 (1351)           
                                       <NA>                                                                         24.8 ( 627)           
  Fat.bin_40 % (freq)                  <40%                                                                         55.1 (1395)    24.8   
                                       >40%                                                                         20.2 ( 511)           
                                       <NA>                                                                         24.8 ( 627)           
  OverallPlaquePhenotype % (freq)      atheromatous                                                                 19.5 ( 495)    25.1   
                                       fibroatheromatous                                                            27.4 ( 694)           
                                       fibrous                                                                      28.0 ( 708)           
                                       <NA>                                                                         25.1 ( 636)           
  SMC_rankNorm (mean (SD))                                                                                        -0.061 (0.962)   32.7   
  MAC_rankNorm (mean (SD))                                                                                         0.179 (0.952)   32.5   
  Neutrophils_rankNorm (mean (SD))                                                                                 0.027 (0.951)   88.0   
  MastCells_rankNorm (mean (SD))                                                                                  -0.010 (1.002)   90.4   
  VesselDensity_rankNorm (mean (SD))                                                                               0.057 (0.981)   37.8   

3.4 Athero-Express Single-Cell RNA Study (AESCRNA)

3.4.1 Baseline summary

metadata <- scRNAseqData@meta.data %>% as_tibble()
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)
distinct: removed 6,154 rows (99%), 37 rows remaining
scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)
[1]   37 1216
# Replace missing data 
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)

na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A", 
                "Not Available", "Not available", 
                "missing", 
                "-999", "-99", 
                "No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.

scRNAseqDataMetaAE %>%
  replace_with_na_all(condition = ~.x %in% na_strings)
cat("====================================================================================================")
====================================================================================================
cat("SELECTION THE SHIZZLE")
SELECTION THE SHIZZLE
cat("- sanity checking PRIOR to selection")
- sanity checking PRIOR to selection
library(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")
        Hospital
Sex      St. Antonius, Nieuwegein UMC Utrecht
  female                        0          11
  male                          0          26
ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")
                                                                                         Artery
Sex                                                                                       female male
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA      0    0
  carotid (left & right)                                                                      11   25
  femoral/iliac (left, right or both sides)                                                    0    0
  other carotid arteries (common, external)                                                    0    1
  carotid bypass and injury (left, right or both sides)                                        0    0
  aneurysmata (carotid & femoral)                                                              0    0
  aorta                                                                                        0    0
  other arteries (renal, popliteal, vertebral)                                                 0    0
  femoral bypass, angioseal and injury (left, right or both sides)                             0    0
ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany")
                                                                                                 ae.gender
ae.ic                                                                                             female male
  missing                                                                                              0    0
  no, died                                                                                             0    0
  yes                                                                                                  6   14
  yes, health treatment when possible                                                                  2    7
  yes, no health treatment                                                                             1    2
  yes, no health treatment, no commercial business                                                     1    2
  yes, no tissue, no commerical business                                                               0    0
  yes, no tissue, no questionnaires, no medical info, no commercial business                           0    0
  yes, no questionnaires, no health treatment, no commercial business                                  0    0
  yes, no questionnaires, health treatment when possible                                               0    0
  yes, no tissue, no questionnaires, no health treatment, no commerical business                       0    0
  yes, no health treatment, no medical info, no commercial business                                    0    0
  yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business      0    0
  yes, no questionnaires, no health treatment                                                          0    0
  yes, no tissue, no health treatment                                                                  0    0
  yes, no tissue, no questionnaires                                                                    0    0
  yes, no tissue, health treatment when possible                                                       0    0
  yes, no tissue                                                                                       0    0
  yes, no commerical business                                                                          0    1
  yes, health treatment when possible, no commercial business                                          0    0
  yes, no medical info, no commercial business                                                         0    0
  yes, no questionnaires                                                                               0    0
  yes, no tissue, no questionnaires, no health treatment, no medical info                              0    0
  yes, no tissue, no questionnaires, no health treatment, no commercial business                       0    0
  yes, no medical info                                                                                 0    0
  yes, no questionnaires, no commercial business                                                       0    0
  yes, no questionnaires, no health treatment, no medical info                                         0    0
  yes, no questionnaires, health treatment when possible, no commercial business                       0    0
  yes,  no health treatment, no medical info                                                           0    0
  no, doesn't want to                                                                                  0    0
  no, unable to sign                                                                                   0    0
  no, no reaction                                                                                      0    0
  no, lost                                                                                             0    0
  no, too old                                                                                          0    0
  yes, no medical info, health treatment when possible                                                 1    0
  no (never asked for IC because there was no tissue)                                                  0    0
  yes, no medical info, no commercial business, health treatment when possible                         0    0
  no, endpoint                                                                                         0    0
  wil niets invullen, wel alles gebruiken                                                              0    0
  second informed concents: yes, no commercial business                                                0    0
  nooit geincludeerd                                                                                   0    0
rm(ae.gender, ae.hospital, ae.artery, ae.ic)


scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
                            (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
                              informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                              informedconsent != "no, died" &
                              informedconsent != "yes, no tissue, no commerical business" &
                              informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                              informedconsent != "yes, no tissue, no health treatment" &
                              informedconsent != "yes, no tissue, no questionnaires" &
                              informedconsent != "yes, no tissue, health treatment when possible" &
                              informedconsent != "yes, no tissue" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                              informedconsent != "no, doesn't want to" &
                              informedconsent != "no, unable to sign" &
                              informedconsent != "no, no reaction" &
                              informedconsent != "no, lost" &
                              informedconsent != "no, too old" &
                              informedconsent != "yes, no medical info, health treatment when possible" & 
                              informedconsent != "no (never asked for IC because there was no tissue)" &
                              informedconsent != "no, endpoint" &
                              informedconsent != "nooit geincludeerd")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)
[1]   36 1216
# DT::datatable(scRNAseqDataMetaAE.all)

Showing the baseline table.

cat("===========================================================================================")
===========================================================================================
cat("CREATE BASELINE TABLE")
CREATE BASELINE TABLE
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  # strata = "Gender",
                                                  data = scRNAseqDataMetaAE.all, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]
Warning in ModuleReturnVarsExist(vars, data) :
  These variables only have NA/NaN: macmean0 smcmean0 neutrophils Mast_cells_plaque IPH.bin vessel_density_averaged SMC_rankNorm MAC_rankNorm Neutrophils_rankNorm MastCells_rankNorm VesselDensity_rankNorm  Dropped
                                 
                                  level                                                                     Overall          
  n                                                                                                              36          
  Hospital (%)                    St. Antonius, Nieuwegein                                                      0.0          
                                  UMC Utrecht                                                                 100.0          
  ORyear (%)                      No data available/missing                                                     0.0          
                                  2002                                                                          0.0          
                                  2003                                                                          0.0          
                                  2004                                                                          0.0          
                                  2005                                                                          0.0          
                                  2006                                                                          0.0          
                                  2007                                                                          0.0          
                                  2008                                                                          0.0          
                                  2009                                                                          0.0          
                                  2010                                                                          0.0          
                                  2011                                                                          0.0          
                                  2012                                                                          0.0          
                                  2013                                                                          0.0          
                                  2014                                                                          0.0          
                                  2015                                                                          0.0          
                                  2016                                                                          0.0          
                                  2017                                                                          0.0          
                                  2018                                                                         63.9          
                                  2019                                                                         36.1          
                                  2020                                                                          0.0          
                                  2021                                                                          0.0          
  Age (mean (SD))                                                                                            72.444 (8.255)  
  Gender (%)                      female                                                                       27.8          
                                  male                                                                         72.2          
  TC_finalCU (mean (SD))                                                                                    167.402 (47.126) 
  LDL_finalCU (mean (SD))                                                                                    95.810 (37.936) 
  HDL_finalCU (mean (SD))                                                                                    43.901 (9.974)  
  TG_finalCU (mean (SD))                                                                                    171.001 (107.745)
  TC_final (mean (SD))                                                                                        4.336 (1.221)  
  LDL_final (mean (SD))                                                                                       2.481 (0.983)  
  HDL_final (mean (SD))                                                                                       1.137 (0.258)  
  TG_final (mean (SD))                                                                                        1.932 (1.218)  
  systolic (mean (SD))                                                                                      152.714 (25.400) 
  diastoli (mean (SD))                                                                                       80.229 (15.904) 
  GFR_MDRD (mean (SD))                                                                                       82.126 (31.316) 
  BMI (mean (SD))                                                                                            26.626 (3.689)  
  KDOQI (%)                       No data available/missing                                                     0.0          
                                  Normal kidney function                                                       33.3          
                                  CKD 2 (Mild)                                                                 30.6          
                                  CKD 3 (Moderate)                                                             25.0          
                                  CKD 4 (Severe)                                                                0.0          
                                  CKD 5 (Failure)                                                               0.0          
                                  <NA>                                                                         11.1          
  BMI_WHO (%)                     No data available/missing                                                     0.0          
                                  Underweight                                                                   2.8          
                                  Normal                                                                       33.3          
                                  Overweight                                                                   41.7          
                                  Obese                                                                        16.7          
                                  <NA>                                                                          5.6          
  SmokerStatus (%)                Current smoker                                                               33.3          
                                  Ex-smoker                                                                    50.0          
                                  Never smoked                                                                 13.9          
                                  <NA>                                                                          2.8          
  AlcoholUse (%)                  No                                                                           38.9          
                                  Yes                                                                          55.6          
                                  <NA>                                                                          5.6          
  DiabetesStatus (%)              Control (no Diabetes Dx/Med)                                                 63.9          
                                  Diabetes                                                                     36.1          
  Hypertension.selfreport (%)     No data available/missing                                                     0.0          
                                  no                                                                           11.1          
                                  yes                                                                          86.1          
                                  <NA>                                                                          2.8          
  Hypertension.selfreportdrug (%) No data available/missing                                                     0.0          
                                  no                                                                           11.1          
                                  yes                                                                          86.1          
                                  <NA>                                                                          2.8          
  Hypertension.composite (%)      No data available/missing                                                     0.0          
                                  no                                                                            5.6          
                                  yes                                                                          94.4          
  Hypertension.drugs (%)          No data available/missing                                                     0.0          
                                  no                                                                            5.6          
                                  yes                                                                          91.7          
                                  <NA>                                                                          2.8          
  Med.anticoagulants (%)          No data available/missing                                                     0.0          
                                  no                                                                           88.9          
                                  yes                                                                           5.6          
                                  <NA>                                                                          5.6          
  Med.all.antiplatelet (%)        No data available/missing                                                     0.0          
                                  no                                                                           25.0          
                                  yes                                                                          72.2          
                                  <NA>                                                                          2.8          
  Med.Statin.LLD (%)              No data available/missing                                                     0.0          
                                  no                                                                           19.4          
                                  yes                                                                          77.8          
                                  <NA>                                                                          2.8          
  Stroke_Dx (%)                   Missing                                                                       0.0          
                                  No stroke diagnosed                                                          50.0          
                                  Stroke diagnosed                                                             50.0          
  sympt (%)                       missing                                                                       0.0          
                                  Asymptomatic                                                                 16.7          
                                  TIA                                                                          13.9          
                                  minor stroke                                                                 30.6          
                                  Major stroke                                                                 11.1          
                                  Amaurosis fugax                                                              13.9          
                                  Four vessel disease                                                           0.0          
                                  Vertebrobasilary TIA                                                          0.0          
                                  Retinal infarction                                                            2.8          
                                  Symptomatic, but aspecific symtoms                                            2.8          
                                  Contralateral symptomatic occlusion                                           0.0          
                                  retinal infarction                                                            2.8          
                                  armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0          
                                  retinal infarction + TIAs                                                     0.0          
                                  Ocular ischemic syndrome                                                      5.6          
                                  ischemisch glaucoom                                                           0.0          
                                  subclavian steal syndrome                                                     0.0          
                                  TGA                                                                           0.0          
  Symptoms.5G (%)                 Asymptomatic                                                                 16.7          
                                  Ocular                                                                       19.4          
                                  Other                                                                         2.8          
                                  Retinal infarction                                                            5.6          
                                  Stroke                                                                       41.7          
                                  TIA                                                                          13.9          
  AsymptSympt (%)                 Asymptomatic                                                                 16.7          
                                  Ocular and others                                                            27.8          
                                  Symptomatic                                                                  55.6          
  restenos (%)                    missing                                                                       0.0          
                                  de novo                                                                     100.0          
                                  restenosis                                                                    0.0          
                                  stenose bij angioseal na PTCA                                                 0.0          
  stenose (%)                     missing                                                                       0.0          
                                  0-49%                                                                         2.8          
                                  50-70%                                                                       16.7          
                                  70-90%                                                                       41.7          
                                  90-99%                                                                       22.2          
                                  100% (Occlusion)                                                              0.0          
                                  NA                                                                            0.0          
                                  50-99%                                                                        0.0          
                                  70-99%                                                                       16.7          
                                  99                                                                            0.0          
  MedHx_CVD (%)                   No                                                                           27.8          
                                  yes                                                                          72.2          
  CAD_history (%)                 Missing                                                                       0.0          
                                  No history CAD                                                               72.2          
                                  History CAD                                                                  27.8          
  PAOD (%)                        missing/no data                                                               0.0          
                                  no                                                                           86.1          
                                  yes                                                                          13.9          
  Peripheral.interv (%)           no                                                                           77.8          
                                  yes                                                                          22.2          
  EP_composite (%)                No data available.                                                            0.0          
                                  No composite endpoints                                                       72.2          
                                  Composite endpoints                                                          11.1          
                                  <NA>                                                                         16.7          
  EP_composite_time (mean (SD))                                                                               1.319 (0.642)  
  Macrophages.bin (%)             no/minor                                                                      2.8          
                                  moderate/heavy                                                                2.8          
                                  <NA>                                                                         94.4          
  SMC.bin (%)                     no/minor                                                                      2.8          
                                  moderate/heavy                                                                2.8          
                                  <NA>                                                                         94.4          
  Calc.bin (%)                    no/minor                                                                      5.6          
                                  moderate/heavy                                                                0.0          
                                  <NA>                                                                         94.4          
  Collagen.bin (%)                no/minor                                                                      0.0          
                                  moderate/heavy                                                                5.6          
                                  <NA>                                                                         94.4          
  Fat.bin_10 (%)                   <10%                                                                         0.0          
                                   >10%                                                                         5.6          
                                  <NA>                                                                         94.4          
  Fat.bin_40 (%)                  <40%                                                                          2.8          
                                  >40%                                                                          2.8          
                                  <NA>                                                                         94.4          
  OverallPlaquePhenotype (%)      atheromatous                                                                  2.8          
                                  fibroatheromatous                                                             2.8          
                                  fibrous                                                                       0.0          
                                  <NA>                                                                         94.4          

3.4.2 Saving baseline for AESCRNA

Writing the baseline table to Excel format.

# Write basetable
require(openxlsx)
write.xlsx(file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.scRNAseq.xlsx"),
           format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE), 
           rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

4 AESCRNA

4.1 Quality control

Here review the number of cells per sample, plate, and patients. And plot the ratio’s per sample and study number.

## check stuff
cat("\nHow many cells per type ...?")

How many cells per type ...?
sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))

  17   16   15   14   13   12   11   10    9    8    7    6    5    4    3    2    1    0 
  31   34   84  110  151  172  190  203  211  225  290  345  437  534  577  626  861 1110 
cat("\n\nHow many cells per plate ...?")


How many cells per plate ...?
sort(table(scRNAseqData@meta.data$ID))

4530.P1 4440.P1 4472.P1 4478.P1 4477.P1 4500.P1 4458.P1 4459.P1 4447.P2 4447.P3 4487.P2 4502.P1 4455.P1 4496.P1 4501.P1 4447.P1 4489.P1 4476.P1 4448.P1 4487.P1 4571.P1 4495.P1 4432.P1 
      4      11      32      40      41      45      47      48      51      66      75      80      82      88      93      96     102     104     105     112     112     115     129 
4520.P1 4450.P2 4545.P1 4513.P1 4452.P3 4453.P3 4452.P2 4450.P1 4558.P1 4535.P1 4488.P1 4480.P1 4470.P1 4450.P3 4453.P1 4486.P1 4452.P1 4546.P1 4443.P2 4491.P1 4453.P2 4530.P2 4443.P1 
    130     134     135     139     141     143     155     157     157     158     159     161     165     166     177     179     183     188     189     193     197     207     209 
4521.P2 4443.P3 4542.P1 
    212     239     240 
cat("\n\nHow many cells per type per plate ...?")


How many cells per type per plate ...?
table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)
    
     4432.P1 4440.P1 4443.P1 4443.P2 4443.P3 4447.P1 4447.P2 4447.P3 4448.P1 4450.P1 4450.P2 4450.P3 4452.P1 4452.P2 4452.P3 4453.P1 4453.P2 4453.P3 4455.P1 4458.P1 4459.P1 4470.P1
  0       28       2      31      56      53       8       7      12      22      15      22      22       5      15      21      19      20      23      10       5       2      44
  1       23       0       0       1       0       0       0       0      27      13      10      11       7      17      14      18      27      17       9       2       9      25
  2       18       1      15      11       8       9       5       4      11      20      23      25       4       8       8       7       4       6      19       8       9      16
  3        2       0       3       4       4       4       3       8       0      27      20      50     134      48      32      42      78      12       2       2       0       2
  4       14       2       5       7       5       2       3       2       6      12       5       5       7      10       6      33      15      19      10       1       2      23
  5       11       3      21      22      28       5       6       5       7       9       4       5       5       6       4      17      14      14       5       6       6       5
  6        1       1      66      46      73      15       1       4       3       4       7       6       4      10      13      18      10      26       3       1       3      20
  7        4       0      14       6       4       8       4       6       3      31      24      10       5       9      14       4       2       5       0       2       1       7
  8        9       0      10       2       5       4       3       7       4       2       5       4       0       2       2       1       3       1       4       2       0       4
  9        3       1       3       2       2       8       5       8       8      14       4       8       2       3       2       0       0       2       1       8       0       0
  10       7       0       5       6       1       2       2       0       2       2       3       6       3       4       2       1       4       2       7       2       2       1
  11       0       1       7       5       2      23      11       8       0       0       5       3       0       8       7       1       2       5       3       2       6       7
  12       4       0       8       7      11       0       0       0       9       0       0       5       3       6       6       2      11       3       3       0       4       7
  13       1       0      20       7      40       6       0       0       1       0       1       1       0       0       1       0       1       0       2       1       1       2
  14       1       0       0       1       1       1       0       1       1       5       1       3       1       2       6       4       3       1       2       3       0       0
  15       0       0       1       5       1       1       1       1       0       1       0       1       3       6       2       4       3       3       1       2       1       2
  16       1       0       0       0       1       0       0       0       0       0       0       0       0       0       1       2       0       3       0       0       2       0
  17       2       0       0       1       0       0       0       0       1       2       0       1       0       1       0       4       0       1       1       0       0       0
    
     4472.P1 4476.P1 4477.P1 4478.P1 4480.P1 4486.P1 4487.P1 4487.P2 4488.P1 4489.P1 4491.P1 4495.P1 4496.P1 4500.P1 4501.P1 4502.P1 4513.P1 4520.P1 4521.P2 4530.P1 4530.P2 4535.P1
  0        1      10      11       7      15      38      15       5      28      30      38      12       7       9      28      25      29      23      43       0      29      32
  1        5      21      12       7      51      21      27      21      44      20      51      29       9       4      28      17      24      20      48       1      12      24
  2       10       8       2       6      16      31      18      11      10       4      11      14      49       5      10       1       9      13      13       1      72      12
  3        0       7       0       1       3       7       0      14       1       0       1       2       3       6       3       2       2       7      14       1       9       1
  4        0       7       2       1       1       7       1       2       4      11      16      15       3       3       2      17      29       8      32       0      17      45
  5        3      13       1       5      17       5      11       4       8       4      22      14       4       8       3       5      17       7      14       1      18      12
  6        0       0       0       0       0       1       9       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
  7        3       3       0       1      12      19       5       0      18       4       2       3       1       1       3       0       1       1       0       0       3       1
  8        3       3       1       4       2      12       7       2       6       9       5       6       1       0       1       2      14      19       1       0      25       2
  9        0       1       0       1       3      14       6       1      17       4       9       1       2       0       1       1       4       7       2       0       2       0
  10       4       4       1       0      12       3       2       1       2       4      18       7       3       0       7       1       4       3       4       0       4      12
  11       1      10       5       1       9       8       0      11       2       3       2       5       0       2       5       2       1       7       5       0       0       2
  12       1       2       5       4       8       4       4       1      16       2       9       1       0       0       0       7       4       0       6       0       0       4
  13       0       1       0       0       0       1       3       0       1       0       3       0       1       0       0       0       0       1      20       0       5       0
  14       0       9       0       1       2       0       1       1       1       3       1       4       1       5       0       0       1      11       2       0       6       7
  15       1       2       0       1       8       6       2       1       0       1       4       1       2       0       1       0       0       1       6       0       0       2
  16       0       2       0       0       2       2       0       0       1       0       0       1       1       2       1       0       0       0       1       0       1       2
  17       0       1       1       0       0       0       1       0       0       3       1       0       1       0       0       0       0       2       1       0       4       0
    
     4542.P1 4545.P1 4546.P1 4558.P1 4571.P1
  0       78      57      44      44      10
  1       41      17      38      28      11
  2       11       1       8      11      30
  3        2       6       3       3       2
  4       13      14      64      22       4
  5       12       3       4       3      11
  6        0       0       0       0       0
  7       23       4       4       7       8
  8        5       8       1       3       9
  9       37       5       2       5       2
  10       5       6      12      19       1
  11       1       1       1       0       0
  12       1       1       2       1       0
  13       3       5       1       6      15
  14       2       6       3       2       4
  15       2       0       1       2       1
  16       3       1       0       0       4
  17       1       0       0       1       0
cat("\n\nHow many cells per patient ...?")


How many cells per patient ...?
sort(table(scRNAseqData@meta.data$Patient))

4440 4472 4478 4477 4500 4458 4459 4502 4455 4496 4501 4489 4476 4448 4571 4495 4432 4520 4545 4513 4558 4535 4488 4480 4470 4486 4487 4546 4491 4530 4521 4447 4542 4450 4452 4453 4443 
  11   32   40   41   45   47   48   80   82   88   93  102  104  105  112  115  129  130  135  139  157  158  159  161  165  179  187  188  193  211  212  213  240  457  479  517  637 
cat("\n\nVisualizing these ratio's per study number and sample ...?")


Visualizing these ratio's per study number and sample ...?
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())
Saving 7.29 x 4.51 in image
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())
Saving 7.29 x 4.51 in image

barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)), 
        cex.axis = 1.0, cex.names = 0.5, las = 1,
        col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
pdf 
  3 
dev.off()
quartz_off_screen 
                2 

barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)), 
        cex.axis = 1.0, cex.names = 0.5, las = 2,
        col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
pdf 
  3 
dev.off()
quartz_off_screen 
                2 

4.2 Visualisations

Let’s project known cellular markers.


UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)


# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("EDN1"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("ACKR1"), cols =  c("#ECECEC", "#DB003F"))


# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols =  c("#ECECEC", "#DB003F"))


# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("CD36"), cols =  c("#ECECEC", "#DB003F"))


# t-cells
FeaturePlot(scRNAseqData, features = c("CD3E"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("CD4"), cols =  c("#ECECEC", "#DB003F"))

# FeaturePlot(scRNAseqData, features = c("CD8"), cols =  c("#ECECEC", "#DB003F"))

# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols =  c("#ECECEC", "#DB003F"))


# mast cells
FeaturePlot(scRNAseqData, features = c("KIT"), cols =  c("#ECECEC", "#DB003F"))


# NK cells
FeaturePlot(scRNAseqData, features = c("NCAM1"), cols =  c("#ECECEC", "#DB003F"))

4.3 Targets of interest

We check whether the targets genes, PCSK9, COL4A1, COL4A2, COL3A, COL2A, LDLR, CD36, were sequenced using our method (STARseq).

Several genes are not present or have different names, these are listed here, and were manually removed from/changed in the list.

  • COL3A, not found
  • COL2A, not found

4.3.1 Expression in cell communities

target_genes_rm <- c("COL3A", "COL2A")

temp = target_genes[!target_genes %in% target_genes_rm]

target_genes_qc <- c(temp)

# VlnPlot(scRNAseqData, features = "LINC01600")

# Make directory for plots
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlots")), 
       dir.create(file.path(PLOT_loc, "/VlnPlots")), 
       FALSE)
[1] FALSE
VLN_loc = paste0(PLOT_loc,"/VlnPlots")

# Make directory for plots
ifelse(!dir.exists(file.path(PLOT_loc, "/DotPlots")), 
       dir.create(file.path(PLOT_loc, "/DotPlots")), 
       FALSE)
[1] FALSE
DOT_loc = paste0(PLOT_loc,"/DotPlots")

# Make directory for plots
ifelse(!dir.exists(file.path(PLOT_loc, "/FeaturePlots")), 
       dir.create(file.path(PLOT_loc, "/FeaturePlots")), 
       FALSE)
[1] FALSE
FEAT_loc = paste0(PLOT_loc,"/FeaturePlots")


for (GENE in target_genes_qc){
  print(paste0("Projecting the expression of ", GENE, "."))

  vp1 <-  VlnPlot(scRNAseqData, features = GENE) + 
    xlab("cell communities") + 
    ylab(bquote("normalized expression")) +
    theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"), 
            axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
            legend.position = "none")
    ggsave(paste0(VLN_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
    ggsave(paste0(VLN_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
  
  print(vp1)
  
}
[1] "Projecting the expression of PCSK9."
Saving 7 x 7 in image
Saving 7 x 7 in image
[1] "Projecting the expression of COL4A1."
Saving 7.29 x 4.51 in image
Saving 7.29 x 4.51 in image
[1] "Projecting the expression of COL4A2."
Saving 7.29 x 4.51 in image
Saving 7.29 x 4.51 in image
[1] "Projecting the expression of LDLR."
Saving 7.29 x 4.51 in image
Saving 7.29 x 4.51 in image
[1] "Projecting the expression of CD36."
Saving 7.29 x 4.51 in image
Saving 7.29 x 4.51 in image

library(RColorBrewer)

p1 <- DotPlot(scRNAseqData, features = target_genes_qc,
        cols = "RdBu")

p1 + theme(axis.text.x = element_text(angle = 45, hjust=1, size = 5))

ggsave(paste0(DOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())
Saving 7.29 x 4.51 in image
ggsave(paste0(DOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())
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rm(p1)

FeaturePlot(scRNAseqData, features = c(target_genes_qc),
            cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            combine = TRUE)

ggsave(paste0(FEAT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
Saving 7.29 x 4.51 in image
ggsave(paste0(FEAT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())
Saving 7.29 x 4.51 in image

for (GENE in target_genes_qc){
  print(paste0("Projecting the expression of ", GENE, "."))

  fp1 <-  FeaturePlot(scRNAseqData, features = GENE, cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            combine = TRUE) + 
    xlab("cell communities") + 
    ylab(bquote("normalized expression")) +
    theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"), 
            axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
            legend.position = "right")
    ggsave(paste0(FEAT_loc, "/", Today, ".FeaturePlot.",GENE,".png"), plot = last_plot())
    ggsave(paste0(FEAT_loc, "/", Today, ".FeaturePlot.",GENE,".ps"), plot = last_plot())
  
  print(fp1)
  
}
[1] "Projecting the expression of PCSK9."
Saving 7.29 x 4.51 in image
Saving 7.29 x 4.51 in image
[1] "Projecting the expression of COL4A1."
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Saving 7.29 x 4.51 in image
[1] "Projecting the expression of COL4A2."
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Saving 7.29 x 4.51 in image
[1] "Projecting the expression of LDLR."
Saving 7.29 x 4.51 in image
Saving 7.29 x 4.51 in image
[1] "Projecting the expression of CD36."
Saving 7.29 x 4.51 in image
Saving 7.29 x 4.51 in image

4.3.2 Differential expression between cell communities

Here we project genes to only the broad cell communities:

  • macrophages
  • endothelial cells
  • smooth muscle cells
  • T-cells
  • B-cells
  • Mast cells
  • NK-cells
  • Mixed cells

4.3.2.1 Macrophages

Comparison between the macrophages cell communities (CD14/CD68+), and all other communities.


MAC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III"), 
                          ident.2 = c(#"CD14+CD68+ M I", 
                                      #"CD14+CD68+ M II", 
                                      #"CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC", 
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 00s      
  |++                                                | 2 % ~47s          
  |++                                                | 3 % ~42s          
  |+++                                               | 4 % ~39s          
  |+++                                               | 5 % ~38s          
  |++++                                              | 6 % ~38s          
  |++++                                              | 7 % ~36s          
  |+++++                                             | 8 % ~35s          
  |+++++                                             | 9 % ~35s          
  |++++++                                            | 10% ~34s          
  |++++++                                            | 11% ~33s          
  |+++++++                                           | 12% ~34s          
  |+++++++                                           | 13% ~33s          
  |++++++++                                          | 14% ~33s          
  |++++++++                                          | 15% ~32s          
  |+++++++++                                         | 16% ~32s          
  |+++++++++                                         | 17% ~31s          
  |++++++++++                                        | 18% ~31s          
  |++++++++++                                        | 19% ~30s          
  |+++++++++++                                       | 20% ~30s          
  |+++++++++++                                       | 21% ~31s          
  |++++++++++++                                      | 22% ~30s          
  |++++++++++++                                      | 23% ~30s          
  |+++++++++++++                                     | 24% ~29s          
  |+++++++++++++                                     | 26% ~29s          
  |++++++++++++++                                    | 27% ~28s          
  |++++++++++++++                                    | 28% ~28s          
  |+++++++++++++++                                   | 29% ~27s          
  |+++++++++++++++                                   | 30% ~27s          
  |++++++++++++++++                                  | 31% ~26s          
  |++++++++++++++++                                  | 32% ~26s          
  |+++++++++++++++++                                 | 33% ~25s          
  |+++++++++++++++++                                 | 34% ~25s          
  |++++++++++++++++++                                | 35% ~25s          
  |++++++++++++++++++                                | 36% ~24s          
  |+++++++++++++++++++                               | 37% ~24s          
  |+++++++++++++++++++                               | 38% ~23s          
  |++++++++++++++++++++                              | 39% ~23s          
  |++++++++++++++++++++                              | 40% ~23s          
  |+++++++++++++++++++++                             | 41% ~23s          
  |+++++++++++++++++++++                             | 42% ~22s          
  |++++++++++++++++++++++                            | 43% ~22s          
  |++++++++++++++++++++++                            | 44% ~21s          
  |+++++++++++++++++++++++                           | 45% ~21s          
  |+++++++++++++++++++++++                           | 46% ~21s          
  |++++++++++++++++++++++++                          | 47% ~20s          
  |++++++++++++++++++++++++                          | 48% ~20s          
  |+++++++++++++++++++++++++                         | 49% ~19s          
  |+++++++++++++++++++++++++                         | 50% ~19s          
  |++++++++++++++++++++++++++                        | 51% ~19s          
  |+++++++++++++++++++++++++++                       | 52% ~18s          
  |+++++++++++++++++++++++++++                       | 53% ~18s          
  |++++++++++++++++++++++++++++                      | 54% ~20s          
  |++++++++++++++++++++++++++++                      | 55% ~20s          
  |+++++++++++++++++++++++++++++                     | 56% ~19s          
  |+++++++++++++++++++++++++++++                     | 57% ~19s          
  |++++++++++++++++++++++++++++++                    | 58% ~18s          
  |++++++++++++++++++++++++++++++                    | 59% ~18s          
  |+++++++++++++++++++++++++++++++                   | 60% ~17s          
  |+++++++++++++++++++++++++++++++                   | 61% ~17s          
  |++++++++++++++++++++++++++++++++                  | 62% ~16s          
  |++++++++++++++++++++++++++++++++                  | 63% ~16s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~15s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~15s          
  |++++++++++++++++++++++++++++++++++                | 66% ~14s          
  |++++++++++++++++++++++++++++++++++                | 67% ~14s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~13s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~13s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~13s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~12s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~12s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~11s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~11s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~10s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~10s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~10s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~09s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=41s  
DT::datatable(MAC.markers)

MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
    lab = rownames(MAC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Macrophage markers\n(Macrophage communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
Warning: One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
# MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"), 
       plot = MAC_Volcano_TargetsA)
Saving 7 x 7 in image

The target results are given below and written to a file.

library(tibble)
MAC.markers <- add_column(MAC.markers, Gene = row.names(MAC.markers), .before = 1)

temp <- MAC.markers[MAC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MAC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

4.3.2.2 Smooth muscle cells

Comparison between the smooth muscle cell communities (ACTA2+), and all other communities.


SMC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("ACTA2+ SMC"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      # "ACTA2+ SMC", 
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 02s      
  |++                                                | 2 % ~59s          
  |++                                                | 3 % ~58s          
  |+++                                               | 4 % ~55s          
  |+++                                               | 5 % ~59s          
  |++++                                              | 6 % ~56s          
  |++++                                              | 7 % ~54s          
  |+++++                                             | 9 % ~53s          
  |+++++                                             | 10% ~51s          
  |++++++                                            | 11% ~53s          
  |++++++                                            | 12% ~55s          
  |+++++++                                           | 13% ~55s          
  |+++++++                                           | 14% ~53s          
  |++++++++                                          | 15% ~52s          
  |++++++++                                          | 16% ~51s          
  |+++++++++                                         | 17% ~50s          
  |++++++++++                                        | 18% ~49s          
  |++++++++++                                        | 19% ~48s          
  |+++++++++++                                       | 20% ~48s          
  |+++++++++++                                       | 21% ~47s          
  |++++++++++++                                      | 22% ~46s          
  |++++++++++++                                      | 23% ~45s          
  |+++++++++++++                                     | 24% ~44s          
  |+++++++++++++                                     | 26% ~44s          
  |++++++++++++++                                    | 27% ~43s          
  |++++++++++++++                                    | 28% ~43s          
  |+++++++++++++++                                   | 29% ~42s          
  |+++++++++++++++                                   | 30% ~42s          
  |++++++++++++++++                                  | 31% ~41s          
  |++++++++++++++++                                  | 32% ~40s          
  |+++++++++++++++++                                 | 33% ~39s          
  |++++++++++++++++++                                | 34% ~39s          
  |++++++++++++++++++                                | 35% ~42s          
  |+++++++++++++++++++                               | 36% ~41s          
  |+++++++++++++++++++                               | 37% ~40s          
  |++++++++++++++++++++                              | 38% ~39s          
  |++++++++++++++++++++                              | 39% ~38s          
  |+++++++++++++++++++++                             | 40% ~37s          
  |+++++++++++++++++++++                             | 41% ~37s          
  |++++++++++++++++++++++                            | 43% ~36s          
  |++++++++++++++++++++++                            | 44% ~35s          
  |+++++++++++++++++++++++                           | 45% ~34s          
  |+++++++++++++++++++++++                           | 46% ~33s          
  |++++++++++++++++++++++++                          | 47% ~32s          
  |++++++++++++++++++++++++                          | 48% ~32s          
  |+++++++++++++++++++++++++                         | 49% ~31s          
  |+++++++++++++++++++++++++                         | 50% ~30s          
  |++++++++++++++++++++++++++                        | 51% ~30s          
  |+++++++++++++++++++++++++++                       | 52% ~29s          
  |+++++++++++++++++++++++++++                       | 53% ~28s          
  |++++++++++++++++++++++++++++                      | 54% ~27s          
  |++++++++++++++++++++++++++++                      | 55% ~27s          
  |+++++++++++++++++++++++++++++                     | 56% ~26s          
  |+++++++++++++++++++++++++++++                     | 57% ~25s          
  |++++++++++++++++++++++++++++++                    | 59% ~24s          
  |++++++++++++++++++++++++++++++                    | 60% ~24s          
  |+++++++++++++++++++++++++++++++                   | 61% ~23s          
  |+++++++++++++++++++++++++++++++                   | 62% ~22s          
  |++++++++++++++++++++++++++++++++                  | 63% ~22s          
  |++++++++++++++++++++++++++++++++                  | 64% ~21s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~21s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~20s          
  |++++++++++++++++++++++++++++++++++                | 67% ~19s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~19s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~18s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~18s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~17s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~16s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~16s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~15s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~14s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~14s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~13s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~12s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~12s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~11s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~10s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~09s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=56s  
DT::datatable(SMC.markers)

SMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
    lab = rownames(SMC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "SMC markers\n(SMC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
Warning: One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
# SMC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"), 
       plot = SMC_Volcano_TargetsA)
Saving 7 x 7 in image

The target results are given below and written to a file.

library(tibble)
SMC.markers <- add_column(SMC.markers, Gene = row.names(SMC.markers), .before = 1)

temp <- SMC.markers[SMC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".SMC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

4.3.2.3 Endothelial cells

Comparison between the endothelial cell communities (CD34+), and all other communities.


EC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD34+ EC I", "CD34+ EC II"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs",
                                      # "CD34+ EC I", 
                                      # "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC",
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 11s      
  |++                                                | 2 % ~58s          
  |++                                                | 3 % ~53s          
  |+++                                               | 4 % ~50s          
  |+++                                               | 5 % ~48s          
  |++++                                              | 6 % ~48s          
  |++++                                              | 8 % ~46s          
  |+++++                                             | 9 % ~45s          
  |+++++                                             | 10% ~44s          
  |++++++                                            | 11% ~44s          
  |++++++                                            | 12% ~43s          
  |+++++++                                           | 13% ~44s          
  |+++++++                                           | 14% ~43s          
  |++++++++                                          | 15% ~42s          
  |+++++++++                                         | 16% ~41s          
  |+++++++++                                         | 17% ~40s          
  |++++++++++                                        | 18% ~39s          
  |++++++++++                                        | 19% ~39s          
  |+++++++++++                                       | 20% ~38s          
  |+++++++++++                                       | 22% ~37s          
  |++++++++++++                                      | 23% ~37s          
  |++++++++++++                                      | 24% ~36s          
  |+++++++++++++                                     | 25% ~36s          
  |+++++++++++++                                     | 26% ~35s          
  |++++++++++++++                                    | 27% ~35s          
  |++++++++++++++                                    | 28% ~34s          
  |+++++++++++++++                                   | 29% ~34s          
  |++++++++++++++++                                  | 30% ~33s          
  |++++++++++++++++                                  | 31% ~33s          
  |+++++++++++++++++                                 | 32% ~32s          
  |+++++++++++++++++                                 | 33% ~32s          
  |++++++++++++++++++                                | 34% ~31s          
  |++++++++++++++++++                                | 35% ~31s          
  |+++++++++++++++++++                               | 37% ~30s          
  |+++++++++++++++++++                               | 38% ~30s          
  |++++++++++++++++++++                              | 39% ~29s          
  |++++++++++++++++++++                              | 40% ~29s          
  |+++++++++++++++++++++                             | 41% ~28s          
  |+++++++++++++++++++++                             | 42% ~28s          
  |++++++++++++++++++++++                            | 43% ~27s          
  |+++++++++++++++++++++++                           | 44% ~27s          
  |+++++++++++++++++++++++                           | 45% ~26s          
  |++++++++++++++++++++++++                          | 46% ~26s          
  |++++++++++++++++++++++++                          | 47% ~25s          
  |+++++++++++++++++++++++++                         | 48% ~25s          
  |+++++++++++++++++++++++++                         | 49% ~24s          
  |++++++++++++++++++++++++++                        | 51% ~24s          
  |++++++++++++++++++++++++++                        | 52% ~23s          
  |+++++++++++++++++++++++++++                       | 53% ~23s          
  |+++++++++++++++++++++++++++                       | 54% ~22s          
  |++++++++++++++++++++++++++++                      | 55% ~22s          
  |++++++++++++++++++++++++++++                      | 56% ~21s          
  |+++++++++++++++++++++++++++++                     | 57% ~20s          
  |++++++++++++++++++++++++++++++                    | 58% ~20s          
  |++++++++++++++++++++++++++++++                    | 59% ~19s          
  |+++++++++++++++++++++++++++++++                   | 60% ~19s          
  |+++++++++++++++++++++++++++++++                   | 61% ~19s          
  |++++++++++++++++++++++++++++++++                  | 62% ~18s          
  |++++++++++++++++++++++++++++++++                  | 63% ~18s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~17s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~17s          
  |++++++++++++++++++++++++++++++++++                | 67% ~16s          
  |++++++++++++++++++++++++++++++++++                | 68% ~16s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~15s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~15s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~14s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~14s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~13s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~13s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~12s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~12s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~11s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~11s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~09s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=51s  
DT::datatable(EC.markers)

EC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
    lab = rownames(EC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Endothelial cell markers\n(EC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
Warning: One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
# EC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"), 
       plot = EC_Volcano_TargetsA)
Saving 7 x 7 in image

The target results are given below and written to a file.

library(tibble)
EC.markers <- add_column(EC.markers, Gene = row.names(EC.markers), .before = 1)

temp <- EC.markers[EC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".EC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

4.3.2.4 T-cells

Comparison between the T-cell communities (CD3/CD4/CD8+), and all other communities.


TC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      # "CD3+CD8+ T I",
                                      # "CD3+CD8A+ T II ", 
                                      # "CD3+CD8A+ T III", 
                                      # "CD3+CD4+ T I", 
                                      # "CD3+CD4+ T II", 
                                      # "CD3 Tregs", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC",
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~43s          
  |++                                                | 2 % ~44s          
  |++                                                | 3 % ~46s          
  |+++                                               | 4 % ~44s          
  |+++                                               | 5 % ~42s          
  |++++                                              | 6 % ~41s          
  |++++                                              | 7 % ~39s          
  |+++++                                             | 8 % ~38s          
  |+++++                                             | 9 % ~36s          
  |++++++                                            | 11% ~35s          
  |++++++                                            | 12% ~35s          
  |+++++++                                           | 13% ~34s          
  |+++++++                                           | 14% ~33s          
  |++++++++                                          | 15% ~33s          
  |++++++++                                          | 16% ~32s          
  |+++++++++                                         | 17% ~31s          
  |+++++++++                                         | 18% ~31s          
  |++++++++++                                        | 19% ~30s          
  |++++++++++                                        | 20% ~31s          
  |+++++++++++                                       | 21% ~30s          
  |++++++++++++                                      | 22% ~30s          
  |++++++++++++                                      | 23% ~29s          
  |+++++++++++++                                     | 24% ~29s          
  |+++++++++++++                                     | 25% ~28s          
  |++++++++++++++                                    | 26% ~28s          
  |++++++++++++++                                    | 27% ~27s          
  |+++++++++++++++                                   | 28% ~27s          
  |+++++++++++++++                                   | 29% ~26s          
  |++++++++++++++++                                  | 31% ~26s          
  |++++++++++++++++                                  | 32% ~25s          
  |+++++++++++++++++                                 | 33% ~25s          
  |+++++++++++++++++                                 | 34% ~24s          
  |++++++++++++++++++                                | 35% ~25s          
  |++++++++++++++++++                                | 36% ~24s          
  |+++++++++++++++++++                               | 37% ~24s          
  |+++++++++++++++++++                               | 38% ~23s          
  |++++++++++++++++++++                              | 39% ~23s          
  |++++++++++++++++++++                              | 40% ~23s          
  |+++++++++++++++++++++                             | 41% ~22s          
  |++++++++++++++++++++++                            | 42% ~22s          
  |++++++++++++++++++++++                            | 43% ~21s          
  |+++++++++++++++++++++++                           | 44% ~21s          
  |+++++++++++++++++++++++                           | 45% ~20s          
  |++++++++++++++++++++++++                          | 46% ~23s          
  |++++++++++++++++++++++++                          | 47% ~23s          
  |+++++++++++++++++++++++++                         | 48% ~22s          
  |+++++++++++++++++++++++++                         | 49% ~22s          
  |++++++++++++++++++++++++++                        | 51% ~21s          
  |++++++++++++++++++++++++++                        | 52% ~21s          
  |+++++++++++++++++++++++++++                       | 53% ~20s          
  |+++++++++++++++++++++++++++                       | 54% ~19s          
  |++++++++++++++++++++++++++++                      | 55% ~19s          
  |++++++++++++++++++++++++++++                      | 56% ~18s          
  |+++++++++++++++++++++++++++++                     | 57% ~18s          
  |+++++++++++++++++++++++++++++                     | 58% ~17s          
  |++++++++++++++++++++++++++++++                    | 59% ~17s          
  |++++++++++++++++++++++++++++++                    | 60% ~16s          
  |+++++++++++++++++++++++++++++++                   | 61% ~16s          
  |++++++++++++++++++++++++++++++++                  | 62% ~16s          
  |++++++++++++++++++++++++++++++++                  | 63% ~15s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~15s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~14s          
  |++++++++++++++++++++++++++++++++++                | 66% ~14s          
  |++++++++++++++++++++++++++++++++++                | 67% ~13s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~13s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~12s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~12s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~12s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~11s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~11s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~10s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~10s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~10s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~09s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~09s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=39s  
DT::datatable(TC.markers)

TC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
    lab = rownames(TC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "T-cell markers\n(T-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
Warning: One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
# TC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"), 
       plot = TC_Volcano_TargetsA)
Saving 7 x 7 in image

The target results are given below and written to a file.

library(tibble)
TC.markers <- add_column(TC.markers, Gene = row.names(TC.markers), .before = 1)

temp <- TC.markers[TC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".TC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

4.3.2.5 B-cells

Comparison between the B-cell communities (CD79A+), and all other communities.


BC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD79A+ B I", 
                                      "CD79A+ B II"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs",
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC",
                                      "NCAM1+ NK", 
                                      "KIT+ MC"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~33s          
  |++                                                | 2 % ~48s          
  |++                                                | 3 % ~42s          
  |+++                                               | 4 % ~37s          
  |+++                                               | 5 % ~34s          
  |++++                                              | 6 % ~32s          
  |++++                                              | 7 % ~30s          
  |+++++                                             | 8 % ~29s          
  |+++++                                             | 9 % ~28s          
  |++++++                                            | 10% ~27s          
  |++++++                                            | 11% ~26s          
  |+++++++                                           | 12% ~25s          
  |+++++++                                           | 13% ~24s          
  |++++++++                                          | 14% ~24s          
  |++++++++                                          | 15% ~23s          
  |+++++++++                                         | 16% ~23s          
  |+++++++++                                         | 17% ~22s          
  |++++++++++                                        | 18% ~22s          
  |++++++++++                                        | 19% ~21s          
  |+++++++++++                                       | 20% ~21s          
  |+++++++++++                                       | 21% ~21s          
  |++++++++++++                                      | 22% ~20s          
  |++++++++++++                                      | 23% ~20s          
  |+++++++++++++                                     | 24% ~21s          
  |+++++++++++++                                     | 26% ~20s          
  |++++++++++++++                                    | 27% ~20s          
  |++++++++++++++                                    | 28% ~20s          
  |+++++++++++++++                                   | 29% ~19s          
  |+++++++++++++++                                   | 30% ~19s          
  |++++++++++++++++                                  | 31% ~18s          
  |++++++++++++++++                                  | 32% ~18s          
  |+++++++++++++++++                                 | 33% ~18s          
  |+++++++++++++++++                                 | 34% ~17s          
  |++++++++++++++++++                                | 35% ~17s          
  |++++++++++++++++++                                | 36% ~17s          
  |+++++++++++++++++++                               | 37% ~16s          
  |+++++++++++++++++++                               | 38% ~16s          
  |++++++++++++++++++++                              | 39% ~16s          
  |++++++++++++++++++++                              | 40% ~16s          
  |+++++++++++++++++++++                             | 41% ~15s          
  |+++++++++++++++++++++                             | 42% ~15s          
  |++++++++++++++++++++++                            | 43% ~15s          
  |++++++++++++++++++++++                            | 44% ~15s          
  |+++++++++++++++++++++++                           | 45% ~14s          
  |+++++++++++++++++++++++                           | 46% ~14s          
  |++++++++++++++++++++++++                          | 47% ~14s          
  |++++++++++++++++++++++++                          | 48% ~14s          
  |+++++++++++++++++++++++++                         | 49% ~13s          
  |+++++++++++++++++++++++++                         | 50% ~13s          
  |++++++++++++++++++++++++++                        | 51% ~13s          
  |+++++++++++++++++++++++++++                       | 52% ~12s          
  |+++++++++++++++++++++++++++                       | 53% ~12s          
  |++++++++++++++++++++++++++++                      | 54% ~12s          
  |++++++++++++++++++++++++++++                      | 55% ~12s          
  |+++++++++++++++++++++++++++++                     | 56% ~11s          
  |+++++++++++++++++++++++++++++                     | 57% ~11s          
  |++++++++++++++++++++++++++++++                    | 58% ~11s          
  |++++++++++++++++++++++++++++++                    | 59% ~11s          
  |+++++++++++++++++++++++++++++++                   | 60% ~10s          
  |+++++++++++++++++++++++++++++++                   | 61% ~10s          
  |++++++++++++++++++++++++++++++++                  | 62% ~10s          
  |++++++++++++++++++++++++++++++++                  | 63% ~10s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~09s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~09s          
  |++++++++++++++++++++++++++++++++++                | 66% ~09s          
  |++++++++++++++++++++++++++++++++++                | 67% ~08s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~08s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~08s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~08s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~07s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~07s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~08s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~08s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~07s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~07s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~07s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~06s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=28s  
DT::datatable(BC.markers)

BC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
    lab = rownames(BC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "B-cell markers\n(B-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
Warning: One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
# BC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"), 
       plot = BC_Volcano_TargetsA)
Saving 7 x 7 in image

The target results are given below and written to a file.

library(tibble)
BC.markers <- add_column(BC.markers, Gene = row.names(BC.markers), .before = 1)

temp <- BC.markers[BC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".BC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

4.3.2.6 Mast cells

Comparison between the mast cell communities (KIT+), and all other communities.


MC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("KIT+ MC"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs",
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC",
                                      "NCAM1+ NK", 
                                      # "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~41s          
  |++                                                | 2 % ~42s          
  |++                                                | 3 % ~42s          
  |+++                                               | 4 % ~46s          
  |+++                                               | 5 % ~44s          
  |++++                                              | 6 % ~42s          
  |++++                                              | 7 % ~41s          
  |+++++                                             | 8 % ~40s          
  |+++++                                             | 9 % ~39s          
  |++++++                                            | 10% ~38s          
  |++++++                                            | 11% ~38s          
  |+++++++                                           | 12% ~37s          
  |+++++++                                           | 13% ~36s          
  |++++++++                                          | 14% ~35s          
  |++++++++                                          | 15% ~35s          
  |+++++++++                                         | 16% ~35s          
  |+++++++++                                         | 17% ~34s          
  |++++++++++                                        | 18% ~35s          
  |++++++++++                                        | 19% ~38s          
  |+++++++++++                                       | 20% ~37s          
  |+++++++++++                                       | 21% ~36s          
  |++++++++++++                                      | 22% ~36s          
  |++++++++++++                                      | 23% ~36s          
  |+++++++++++++                                     | 24% ~35s          
  |+++++++++++++                                     | 25% ~35s          
  |++++++++++++++                                    | 26% ~34s          
  |++++++++++++++                                    | 27% ~33s          
  |+++++++++++++++                                   | 28% ~32s          
  |+++++++++++++++                                   | 29% ~32s          
  |++++++++++++++++                                  | 30% ~32s          
  |++++++++++++++++                                  | 31% ~32s          
  |+++++++++++++++++                                 | 32% ~31s          
  |+++++++++++++++++                                 | 33% ~31s          
  |++++++++++++++++++                                | 34% ~30s          
  |++++++++++++++++++                                | 35% ~30s          
  |+++++++++++++++++++                               | 36% ~29s          
  |+++++++++++++++++++                               | 37% ~28s          
  |++++++++++++++++++++                              | 38% ~28s          
  |++++++++++++++++++++                              | 39% ~27s          
  |+++++++++++++++++++++                             | 40% ~27s          
  |+++++++++++++++++++++                             | 41% ~27s          
  |++++++++++++++++++++++                            | 42% ~26s          
  |++++++++++++++++++++++                            | 43% ~26s          
  |+++++++++++++++++++++++                           | 44% ~25s          
  |+++++++++++++++++++++++                           | 45% ~24s          
  |++++++++++++++++++++++++                          | 46% ~24s          
  |++++++++++++++++++++++++                          | 47% ~23s          
  |+++++++++++++++++++++++++                         | 48% ~23s          
  |+++++++++++++++++++++++++                         | 49% ~22s          
  |++++++++++++++++++++++++++                        | 51% ~22s          
  |++++++++++++++++++++++++++                        | 52% ~23s          
  |+++++++++++++++++++++++++++                       | 53% ~22s          
  |+++++++++++++++++++++++++++                       | 54% ~22s          
  |++++++++++++++++++++++++++++                      | 55% ~21s          
  |++++++++++++++++++++++++++++                      | 56% ~21s          
  |+++++++++++++++++++++++++++++                     | 57% ~20s          
  |+++++++++++++++++++++++++++++                     | 58% ~20s          
  |++++++++++++++++++++++++++++++                    | 59% ~19s          
  |++++++++++++++++++++++++++++++                    | 60% ~19s          
  |+++++++++++++++++++++++++++++++                   | 61% ~18s          
  |+++++++++++++++++++++++++++++++                   | 62% ~18s          
  |++++++++++++++++++++++++++++++++                  | 63% ~17s          
  |++++++++++++++++++++++++++++++++                  | 64% ~17s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~16s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~16s          
  |++++++++++++++++++++++++++++++++++                | 67% ~15s          
  |++++++++++++++++++++++++++++++++++                | 68% ~15s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~15s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~14s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~14s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~14s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~13s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~13s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~12s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~12s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~11s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~11s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~10s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~09s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=52s  
DT::datatable(MC.markers)

MC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
    lab = rownames(MC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Mast cell markers\n(Mast cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
Warning: One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
# MC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"), 
       plot = MC_Volcano_TargetsA)
Saving 7 x 7 in image

The target results are given below and written to a file.

library(tibble)
MC.markers <- add_column(MC.markers, Gene = row.names(MC.markers), .before = 1)

temp <- MC.markers[MC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

4.3.2.7 NK-cells

Comparison between the natural killer cell communities (NCAM1+), and all other communities.


NK.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("NCAM1+ NK"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs",
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I",
                                      "Mixed II",
                                      "ACTA2+ SMC", 
                                      # "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~24s          
  |++                                                | 2 % ~27s          
  |++                                                | 3 % ~26s          
  |+++                                               | 4 % ~27s          
  |+++                                               | 5 % ~26s          
  |++++                                              | 6 % ~25s          
  |++++                                              | 7 % ~24s          
  |+++++                                             | 8 % ~24s          
  |+++++                                             | 9 % ~23s          
  |++++++                                            | 10% ~23s          
  |++++++                                            | 11% ~22s          
  |+++++++                                           | 12% ~22s          
  |+++++++                                           | 13% ~21s          
  |++++++++                                          | 14% ~21s          
  |++++++++                                          | 15% ~21s          
  |+++++++++                                         | 16% ~20s          
  |+++++++++                                         | 17% ~20s          
  |++++++++++                                        | 18% ~20s          
  |++++++++++                                        | 19% ~19s          
  |+++++++++++                                       | 20% ~19s          
  |+++++++++++                                       | 21% ~18s          
  |++++++++++++                                      | 22% ~18s          
  |++++++++++++                                      | 23% ~18s          
  |+++++++++++++                                     | 24% ~18s          
  |+++++++++++++                                     | 25% ~18s          
  |++++++++++++++                                    | 26% ~17s          
  |++++++++++++++                                    | 27% ~18s          
  |+++++++++++++++                                   | 28% ~18s          
  |+++++++++++++++                                   | 29% ~18s          
  |++++++++++++++++                                  | 30% ~17s          
  |++++++++++++++++                                  | 31% ~17s          
  |+++++++++++++++++                                 | 32% ~17s          
  |+++++++++++++++++                                 | 33% ~17s          
  |++++++++++++++++++                                | 34% ~16s          
  |++++++++++++++++++                                | 35% ~16s          
  |+++++++++++++++++++                               | 36% ~16s          
  |+++++++++++++++++++                               | 37% ~16s          
  |++++++++++++++++++++                              | 38% ~15s          
  |++++++++++++++++++++                              | 39% ~15s          
  |+++++++++++++++++++++                             | 40% ~15s          
  |+++++++++++++++++++++                             | 41% ~15s          
  |++++++++++++++++++++++                            | 42% ~14s          
  |++++++++++++++++++++++                            | 43% ~14s          
  |+++++++++++++++++++++++                           | 44% ~14s          
  |+++++++++++++++++++++++                           | 45% ~14s          
  |++++++++++++++++++++++++                          | 46% ~13s          
  |++++++++++++++++++++++++                          | 47% ~13s          
  |+++++++++++++++++++++++++                         | 48% ~13s          
  |+++++++++++++++++++++++++                         | 49% ~12s          
  |++++++++++++++++++++++++++                        | 51% ~12s          
  |++++++++++++++++++++++++++                        | 52% ~12s          
  |+++++++++++++++++++++++++++                       | 53% ~12s          
  |+++++++++++++++++++++++++++                       | 54% ~11s          
  |++++++++++++++++++++++++++++                      | 55% ~11s          
  |++++++++++++++++++++++++++++                      | 56% ~11s          
  |+++++++++++++++++++++++++++++                     | 57% ~11s          
  |+++++++++++++++++++++++++++++                     | 58% ~10s          
  |++++++++++++++++++++++++++++++                    | 59% ~10s          
  |++++++++++++++++++++++++++++++                    | 60% ~10s          
  |+++++++++++++++++++++++++++++++                   | 61% ~10s          
  |+++++++++++++++++++++++++++++++                   | 62% ~09s          
  |++++++++++++++++++++++++++++++++                  | 63% ~09s          
  |++++++++++++++++++++++++++++++++                  | 64% ~09s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~08s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~08s          
  |++++++++++++++++++++++++++++++++++                | 67% ~08s          
  |++++++++++++++++++++++++++++++++++                | 68% ~08s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~07s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~07s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~07s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~07s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~07s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~06s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~06s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~06s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~06s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~05s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~05s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=24s  
DT::datatable(NK.markers)

NK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
    lab = rownames(NK.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "NK markers\n(NK-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
# NK_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"), 
       plot = NK_Volcano_TargetsA)
Saving 7 x 7 in image

The target results are given below and written to a file.

library(tibble)
NK.markers <- add_column(NK.markers, Gene = row.names(NK.markers), .before = 1)

temp <- NK.markers[NK.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".NK.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

4.3.2.8 Mixed cells

Comparison between the mixed cell communities, and all other communities.


MIXED.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("Mixed I", 
                                      "Mixed II"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs",
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      # "Mixed I", 
                                      # "Mixed II", 
                                      "ACTA2+ SMC", 
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 31s      
  |++                                                | 2 % ~01m 25s      
  |++                                                | 3 % ~01m 20s      
  |+++                                               | 4 % ~01m 15s      
  |+++                                               | 5 % ~01m 12s      
  |++++                                              | 6 % ~01m 09s      
  |++++                                              | 7 % ~01m 08s      
  |+++++                                             | 8 % ~01m 07s      
  |+++++                                             | 9 % ~01m 05s      
  |++++++                                            | 10% ~01m 04s      
  |++++++                                            | 11% ~01m 02s      
  |+++++++                                           | 12% ~01m 01s      
  |+++++++                                           | 13% ~01m 00s      
  |++++++++                                          | 14% ~01m 01s      
  |++++++++                                          | 15% ~01m 00s      
  |+++++++++                                         | 16% ~59s          
  |+++++++++                                         | 17% ~58s          
  |++++++++++                                        | 18% ~57s          
  |++++++++++                                        | 19% ~56s          
  |+++++++++++                                       | 20% ~55s          
  |+++++++++++                                       | 21% ~54s          
  |++++++++++++                                      | 22% ~53s          
  |++++++++++++                                      | 23% ~52s          
  |+++++++++++++                                     | 24% ~51s          
  |+++++++++++++                                     | 26% ~50s          
  |++++++++++++++                                    | 27% ~50s          
  |++++++++++++++                                    | 28% ~49s          
  |+++++++++++++++                                   | 29% ~49s          
  |+++++++++++++++                                   | 30% ~48s          
  |++++++++++++++++                                  | 31% ~47s          
  |++++++++++++++++                                  | 32% ~46s          
  |+++++++++++++++++                                 | 33% ~45s          
  |+++++++++++++++++                                 | 34% ~44s          
  |++++++++++++++++++                                | 35% ~43s          
  |++++++++++++++++++                                | 36% ~43s          
  |+++++++++++++++++++                               | 37% ~43s          
  |+++++++++++++++++++                               | 38% ~42s          
  |++++++++++++++++++++                              | 39% ~41s          
  |++++++++++++++++++++                              | 40% ~40s          
  |+++++++++++++++++++++                             | 41% ~40s          
  |+++++++++++++++++++++                             | 42% ~39s          
  |++++++++++++++++++++++                            | 43% ~38s          
  |++++++++++++++++++++++                            | 44% ~37s          
  |+++++++++++++++++++++++                           | 45% ~36s          
  |+++++++++++++++++++++++                           | 46% ~36s          
  |++++++++++++++++++++++++                          | 47% ~35s          
  |++++++++++++++++++++++++                          | 48% ~34s          
  |+++++++++++++++++++++++++                         | 49% ~33s          
  |+++++++++++++++++++++++++                         | 50% ~33s          
  |++++++++++++++++++++++++++                        | 51% ~32s          
  |+++++++++++++++++++++++++++                       | 52% ~31s          
  |+++++++++++++++++++++++++++                       | 53% ~31s          
  |++++++++++++++++++++++++++++                      | 54% ~30s          
  |++++++++++++++++++++++++++++                      | 55% ~29s          
  |+++++++++++++++++++++++++++++                     | 56% ~29s          
  |+++++++++++++++++++++++++++++                     | 57% ~28s          
  |++++++++++++++++++++++++++++++                    | 58% ~27s          
  |++++++++++++++++++++++++++++++                    | 59% ~27s          
  |+++++++++++++++++++++++++++++++                   | 60% ~26s          
  |+++++++++++++++++++++++++++++++                   | 61% ~25s          
  |++++++++++++++++++++++++++++++++                  | 62% ~25s          
  |++++++++++++++++++++++++++++++++                  | 63% ~24s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~24s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~23s          
  |++++++++++++++++++++++++++++++++++                | 66% ~23s          
  |++++++++++++++++++++++++++++++++++                | 67% ~22s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~21s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~21s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~20s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~19s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~19s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~18s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~18s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~17s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~16s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~16s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~15s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~14s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~13s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~13s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~12s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~11s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~11s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~10s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~09s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 09s
DT::datatable(MIXED.markers)

MIXED_Volcano_TargetsA = EnhancedVolcano(MIXED.markers,
    lab = rownames(MIXED.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Mixed markers\n(Mixed cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
Warning: One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
# MIXED_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MIXED.DEG.Targets.pdf"), 
       plot = MIXED_Volcano_TargetsA)
Saving 7 x 7 in image

The target results are given below and written to a file.

library(tibble)
MIXED.markers <- add_column(MIXED.markers, Gene = row.names(MIXED.markers), .before = 1)

temp <- MIXED.markers[MIXED.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MIXED.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

5 Session information


Version:      v1.1.1
Last update:  2021-10-29
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

Change log
* v1.1.1 Update on the AEDB.
* v1.1.0 Major overhaul; update to WORCS system. Also including multiple options for scRNAseq datasets.
* v1.0.4 Small bug fixes.
* v1.0.3 Fixed weight further by excluding some graphs from the Rmd - obviously these can be added with sharing with third parties, but these are too heavy for a template.
* v1.0.2 Fixed weight of files (limit of 10Mb per file for templates). 
* v1.0.1 Updated background information.
* v1.0.0 Initial version.

sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.0.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] parallel  stats4    tools     stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] RColorBrewer_1.1-2     labelled_2.8.0         openxlsx_4.2.4         SeuratObject_4.0.2     Seurat_4.0.3           devtools_2.4.2         usethis_2.0.1         
 [8] tableone_0.13.0        haven_2.4.3            EnhancedVolcano_1.10.0 ggrepel_0.9.1          mygene_1.28.0          GenomicFeatures_1.44.2 GenomicRanges_1.44.0  
[15] GenomeInfoDb_1.28.4    org.Hs.eg.db_3.13.0    AnnotationDbi_1.54.1   IRanges_2.26.0         S4Vectors_0.30.1       Biobase_2.52.0         BiocGenerics_0.38.0   
[22] DT_0.18                knitr_1.33             forcats_0.5.1          stringr_1.4.0          purrr_0.3.4            tibble_3.1.3           ggplot2_3.3.5         
[29] tidyverse_1.3.1        data.table_1.14.0      naniar_0.6.1           tidylog_1.0.2          tidyr_1.1.3            dplyr_1.0.7            optparse_1.6.6        
[36] readr_2.0.0           

loaded via a namespace (and not attached):
  [1] Hmisc_4.5-0                 ica_1.0-2                   class_7.3-19                ps_1.6.0                    Rsamtools_2.8.0             lmtest_0.9-38              
  [7] rprojroot_2.0.2             crayon_1.4.1                spatstat.core_2.3-0         MASS_7.3-54                 nlme_3.1-152                backports_1.2.1            
 [13] reprex_2.0.1                rlang_0.4.11                XVector_0.32.0              ROCR_1.0-11                 readxl_1.3.1                performance_0.7.3          
 [19] irlba_2.3.3                 nloptr_1.2.2.2              extrafontdb_1.0             callr_3.7.0                 filelock_1.0.2              proto_1.0.0                
 [25] extrafont_0.17              BiocParallel_1.26.2         rjson_0.2.20                bit64_4.0.5                 glue_1.4.2                  sjPlot_2.8.9               
 [31] sctransform_0.3.2           processx_3.5.2              vipor_0.4.5                 spatstat.sparse_2.0-0       UpSetR_1.4.0                spatstat.geom_2.2-2        
 [37] tidyselect_1.1.1            SummarizedExperiment_1.22.0 rio_0.5.27                  fitdistrplus_1.1-5          XML_3.99-0.8                zoo_1.8-9                  
 [43] proj4_1.0-10.1              ggpubr_0.4.0                sjmisc_2.8.7                GenomicAlignments_1.28.0    chron_2.3-56                xtable_1.8-4               
 [49] magrittr_2.0.1              evaluate_0.14               cli_3.0.1                   zlibbioc_1.38.0             rstudioapi_0.13             miniUI_0.1.1.1             
 [55] bslib_0.2.5.1               rpart_4.1-15                sjlabelled_1.1.8            maps_3.4.0                  shiny_1.6.0                 xfun_0.25                  
 [61] parameters_0.14.0           pkgbuild_1.2.0              cluster_2.1.2               KEGGREST_1.32.0             listenv_0.8.0               Biostrings_2.60.2          
 [67] png_0.1-7                   future_1.21.0               withr_2.4.2                 bitops_1.0-7                plyr_1.8.6                  cellranger_1.1.0           
 [73] e1071_1.7-9                 survey_4.1-1                coda_0.19-4                 pillar_1.6.2                cachem_1.0.5                fs_1.5.0                   
 [79] vctrs_0.3.8                 ellipsis_0.3.2              generics_0.1.0              gsubfn_0.7                  foreign_0.8-81              beeswarm_0.4.0             
 [85] munsell_0.5.0               proxy_0.4-26                emmeans_1.6.2-1             DelayedArray_0.18.0         fastmap_1.1.0               compiler_4.1.1             
 [91] pkgload_1.2.1               abind_1.4-5                 httpuv_1.6.1                rtracklayer_1.52.1          sessioninfo_1.1.1           plotly_4.9.4.1             
 [97] GenomeInfoDbData_1.2.6      gridExtra_2.3               lattice_0.20-44             deldir_0.2-10               utf8_1.2.2                  later_1.2.0                
[103] BiocFileCache_2.0.0         jsonlite_1.7.2              scales_1.1.1                pbapply_1.4-3               carData_3.0-4               estimability_1.3           
[109] renv_0.14.0                 lazyeval_0.2.2              promises_1.2.0.1            car_3.0-11                  latticeExtra_0.6-29         goftest_1.2-2              
[115] spatstat.utils_2.2-0        reticulate_1.20             effectsize_0.4.5            checkmate_2.0.0             rmarkdown_2.10              ash_1.0-15                 
[121] cowplot_1.1.1               textshaping_0.3.5           Rtsne_0.15                  pander_0.6.4                uwot_0.1.10                 igraph_1.2.6               
[127] survival_3.2-11             yaml_2.2.1                  systemfonts_1.0.2           htmltools_0.5.1.1           memoise_2.0.0               BiocIO_1.2.0               
[133] viridisLite_0.4.0           digest_0.6.27               assertthat_0.2.1            mime_0.11                   rappdirs_0.3.3              Rttf2pt1_1.3.9             
[139] bayestestR_0.10.5           RSQLite_2.2.8               sqldf_0.4-11                future.apply_1.7.0          remotes_2.4.0               blob_1.2.2                 
[145] ragg_1.1.3                  labeling_0.4.2              splines_4.1.1               Formula_1.2-4               RCurl_1.98-1.5              broom_0.7.9                
[151] hms_1.1.0                   modelr_0.1.8                colorspace_2.0-2            base64enc_0.1-3             BiocManager_1.30.16         ggbeeswarm_0.6.0           
[157] ggrastr_0.2.3               nnet_7.3-16                 sass_0.4.0                  Rcpp_1.0.7                  RANN_2.6.1                  mvtnorm_1.1-2              
[163] clisymbols_1.2.0            fansi_0.5.0                 tzdb_0.1.2                  parallelly_1.27.0           R6_2.5.0                    grid_4.1.1                 
[169] ggridges_0.5.3              lifecycle_1.0.0             zip_2.2.0                   datawizard_0.1.0            curl_4.3.2                  ggsignif_0.6.2             
[175] minqa_1.2.4                 jquerylib_0.1.4             leiden_0.3.9                testthat_3.0.4              getopt_1.20.3               Matrix_1.3-4               
[181] desc_1.3.0                  RcppAnnoy_0.0.19            htmlwidgets_1.5.3           polyclip_1.10-0             biomaRt_2.48.3              crosstalk_1.1.1            
[187] rvest_1.0.1                 mgcv_1.8-36                 globals_0.14.0              insight_0.14.2              htmlTable_2.2.1             patchwork_1.1.0.9000       
[193] codetools_0.2-18            matrixStats_0.61.0          lubridate_1.7.10            prettyunits_1.1.1           dbplyr_2.1.1                gtable_0.3.0               
[199] DBI_1.1.1                   visdat_0.5.3                tensor_1.5                  httr_1.4.2                  KernSmooth_2.23-20          stringi_1.7.3              
[205] progress_1.2.2              farver_2.1.0                reshape2_1.4.4              xml2_1.3.2                  boot_1.3-28                 ggeffects_1.1.1            
[211] ggalt_0.4.0                 lme4_1.1-27.1               restfulr_0.0.13             scattermore_0.7             bit_4.0.4                   sjstats_0.18.1             
[217] jpeg_0.1-9                  MatrixGenerics_1.4.3        spatstat.data_2.1-0         pkgconfig_2.0.3             rstatix_0.7.0               mitools_2.4                

6 Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".scrnaseq_results.RData"))
© 1979-2021 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io.
---
title: "Mapping genes of interest at single-cell resolution in carotid plaques."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan'
date: '`r Sys.Date()`'
output:
  html_notebook: 
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    number_sections: yes
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Helvetica
subtitle: A 'druggable-MI-targets' project
editor_options:
  chunk_output_type: inline
  markdown: 
    wrap: 80
---

```{r global_options, include=FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

*Clean the environment.*

```{r ClearEnvironment, echo = FALSE}
rm(list = ls())
```

*Set locations, and the working directory ...*

```{r LocalSystem, echo = FALSE}
### Operating System Version
### MacBook Pro
# ROOT_loc = "/Users/swvanderlaan/OneDrive - UMC Utrecht"

### MacBook
ROOT_loc = "/Users/slaan3/OneDrive - UMC Utrecht"
GENOMIC_loc = paste0(ROOT_loc, "/Genomics")
STORAGE_loc = "/Volumes/LaCie"

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
PLINK_loc=paste0(STORAGE_loc,"/PLINK")

AEGSQC_loc =  paste0(PLINK_loc, "/_AE_ORIGINALS/AEGS_COMBINED_QC2018")
MICHIMP_loc=paste0(PLINK_loc,"/_AE_ORIGINALS/AEGS_COMBINED_EAGLE2_1000Gp3v5HRCr11")

RAWDATA = paste0(PLINK_loc, "/_AE_ORIGINALS/AESCRNA/prepped_data")

PROJECTROOT_loc = paste0(PLINK_loc, "/analyses/lookups/AE_TEMPLATE")
RESULTS = paste0(PROJECTROOT_loc, "/scRNAseq")
PROJECT_loc = paste0(PROJECTROOT_loc, "/scRNAseq")

# use this if there is relevant information here.
TARGET_loc = paste0(GENOMIC_loc, "/Athero-Express/Forms/[YEAR]/AE_TEMPLATE")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="AESCRNA"
TARGET_GENES = "PCSK9" # Phenotype
TARGET_A="PCSK9"
TARGET_B="COL4A2"

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

*... a package-installation function ...*

```{r Function: installations, echo=FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

*... and load those packages.*

```{r Setting: loading_packages, echo=FALSE, message=FALSE, warning=FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("tidylog")
library("tidylog", warn.conflicts = FALSE)
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")

install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("EnhancedVolcano")

install.packages.auto("haven")
install.packages.auto("tableone")


# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# Replace '2.3.4' with your desired version
# devtools::install_version(package = 'Seurat', version = package_version('2.3.4'))
install.packages.auto("Seurat") # latest version
library("Seurat")

```

*We will create a datestamp and define the Utrecht Science Park Colour Scheme*.

```{r Setting: Colors, echo=FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```

# ERA-CVD 'druggable-MI-targets'

<!-- ![ERA-CVD logo]("Users/swvanderlaan/iCloud/Genomics/Projects/#Druggable-MI-Genes/Administration/ERA-CVD\ Logo_CMYK.jpg") -->

For the ERA-CVD 'druggable-MI-targets' project (grantnumber: 01KL1802) we will
perform two related RNA sequencing (RNAseq) experiments:

1)  conventional ('bulk') RNAseq using RNA extracted from carotid plaque
    samples, n ± 700. As of `r Today.Report` all samples have been selected and
    RNA has been extracted; quality control (QC) was performed and we have a
    dataset of 635 samples.

2)  single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20
    males). As of `r Today.Report` data is available of 40 samples (3 females,
    15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the
[Athero-Express Biobank Study](http:www/atheroexpress.nl) which is an ongoing
study in the UMC Utrecht.

# Background

Collaboration to study gene expression of `r TARGET_GENES` in relation to
atherosclerotic plaques characteristics. The main list of genes are given below.

-   `Genes.xlsx`

```{r targets}
library(openxlsx)

# Manual option
# gene_list <- c("PCSK9", "COL4A1", "COL4A2", "COL3A", "COL2A", "LDLR", "CD36")
# gene_list

gene_list <- read.xlsx(paste0(PROJECTROOT_loc, "/SNP/Genes.xlsx"), sheet = "Genes")

DT::datatable(gene_list)

target_genes <- unlist(gene_list$Gene)
target_genes

```

# Load data

First we will load the data:

-   scRNAseq experimental data and rename the cell types.
-   Athero-Express clinical data.

## AESCRNA: single-cell RNAseq from carotid plaques

Here we load the latest dataset from our Athero-Express Single Cell RNA
experiment.

There are few datasets available:

-   20210316_CircRes2020_18pts.RDS \> the data associated with [Depuydt M.A.C et
    al.](https://doi.org/10.1161/CIRCRESAHA.120.316770)
-   20200701_seurat_37_pts.RDS \> the data of 37 patients
-   20210217_PlaqView_38_pts.RDS \> the data associated with
    [PlaqView](https://www.plaqview.com); this can *not* be couple to study
    numbers
-   20210811_46_patients_Koen.RDS \> the latest dataset - NOTE: failes to open
    'unknown input format'

Here we use the PlaqView data.

```{r LoadData}

scRNAseqData <- readRDS(paste0(RAWDATA, "/Seuset_40_patients/seurat_37_pts_20200701.RDS"))
scRNAseqData
N_GENES=18283
```

The naming/classification is based on a combination conventional markers. We do
not claim to know the exact identity of each cell, rather we refer to cells as
'KIT+ Mast cells"-like cells. Likewise we refer to the cell clusters as
'communities' of cells that exihibit similar properties, *i.e.* similar defining
markers (*e.g. KIT*).

We will rename the cell types to human readable names.

```{r Change cell cummunity names}
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")

levels(unique(scRNAseqData@active.ident))
# [1] "CD3+CD8A+ T cells I"         "CD3+CD8A+ T cells III"       "CD3+CD4+ T Cells I"          "CD14+CD68+ Macrophages I"   
# [5] "Mixed Cells I"               "CD3+CD8A+ T Cells II"        "CD14+CD68+ Macrophages II"   "CD3+CD4+ T Cells II"        
# [9] "ACTA2+ Smooth Muscle Cells"  "CD34+ Endothelial Cells I"   "CD34+ Endothelial Cells II"  "NCAM1+ Natural Killer Cells"
#[13] "Mixed Cells II"              "CD79A+ B Cells I"            "CD14+CD68+ Macrophages III"  "CD3+ Regulatory T Cells"    
#[17] "KIT+ Mast Cells"             "CD79A+ B Cells II"  

celltypes <- c("CD14+CD68+ Macrophages I" = "CD14+CD68+ M I", 
               "CD14+CD68+ Macrophages II" = "CD14+CD68+ M II", 
               "CD14+CD68+ Macrophages III" = "CD14+CD68+ M III",
               "CD3+CD8A+ T cells I" = "CD3+CD8+ T I",
               "CD3+CD8A+ T Cells II" = "CD3+CD8A+ T II",
               "CD3+CD8A+ T cells III" = "CD3+CD8A+ T III",
               "CD3+CD4+ T Cells I" = "CD3+CD4+ T I", 
               "CD3+CD4+ T Cells II" = "CD3+CD4+ T II", 
               "CD3+ Regulatory T Cells" = "CD3 Tregs", 
               "CD34+ Endothelial Cells I" = "CD34+ EC I", 
               "CD34+ Endothelial Cells II" = "CD34+ EC II", 
               "Mixed Cells I" = "Mixed I", 
               "Mixed Cells II" = "Mixed II", 
               "ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC", 
               "NCAM1+ Natural Killer Cells" = "NCAM1+ NK", 
               "KIT+ Mast Cells" = "KIT+ MC",
               "CD79A+ B Cells I" = "CD79A+ B I", 
               "CD79A+ B Cells II" = "CD79A+ B II")

scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData, 
                                       celltypes)
```

```{r Change cell cummunity names - new plot}
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

```

## Clinical data

Loading Athero-Express clinical data.

```{r LoadAEDB}
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2021_1_NEW_AtheroExpressDatabase_ScientificAE_01-02-2021.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2021_3_NEW_AtheroExpressDatabase_ScientificAE_10-09-2021.sav"))


```

### Fixing and creating variables

We need to be very strict in defining *symptoms.* Therefore we will fix a new
variable that groups *symptoms* at inclusion.

Coding of *symptoms* is as follows:

-   missing -999\
-   Asymptomatic 0\
-   TIA 1\
-   minor stroke 2\
-   Major stroke 3\
-   Amaurosis fugax 4\
-   Four vessel disease 5\
-   Vertebrobasilary TIA 7\
-   Retinal infarction 8\
-   Symptomatic, but aspecific symtoms 9
-   Contralateral symptomatic occlusion 10\
-   retinal infarction 11\
-   armclaudication due to occlusion subclavian artery, CEA needed for bypass
    12\
-   retinal infarction + TIAs 13\
-   Ocular ischemic syndrome 14\
-   ischemisch glaucoom 15\
-   subclavian steal syndrome 16\
-   TGA 17

We will group as follows in `Symptoms.5G`:

1.  Asymptomatic \> 0
2.  TIA \> 1, 7, 13
3.  Stroke \> 2, 3
4.  Ocular \> 4, 14, 15
5.  Retinal infarction \> 8, 11
6.  Other \> 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1.  Asymptomatic \> 0
2.  TIA \> 1, 7, 13 + Stroke \> 2, 3
3.  Ocular \> 4, 14, 15 + Retinal infarction \> 8, 11 + Other \> 5, 9, 10, 12,
    16, 17

We will also group as follows in `AsymptSympt2G`:

1.  Asymptomatic \> 0
2.  TIA \> 1, 7, 13 + Stroke \> 2, 3 Ocular \> 4, 14, 15 + Retinal infarction \>
    8, 11 + Other \> 5, 9, 10, 12, 16, 17

```{r FixSymptoms, message=FALSE, warning=FALSE}
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

```

We will also fix the *plaquephenotypes* variable.

Coding of symptoms is as follows:

-   missing -999\
-   not relevant -888
-   fibrous 1\
-   fibroatheromatous 2\
-   atheromatous 3

```{r FixPlaquePhenotypes, message=FALSE, warning=FALSE}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the *diabetes* status variable. We define diabetes as history
of a diagnosis and/or use of glucose-lowering medications.

```{r FixDiabetes, message=FALSE, warning=FALSE}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

table(AEDB$DiabetesStatus)


# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the *smoking* status variable. We are interested in whether
someone never, ever or is currently (at the time of inclusion) smoking. This is
based on the questionnaire.

-   `diet801`: are you a smoker?
-   `diet802`: did you smoke in the past?

We already have some variables indicating smoking status:

-   `SmokingReported`: patient has reported to smoke.
-   `SmokingYearOR`: smoking in the year of surgery?
-   `SmokerCurrent`: currently smoking?

```{r FixSmoking, message=FALSE, warning=FALSE}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix the *alcohol* status variable.

```{r FixAlcohol, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix a history of CAD, stroke or peripheral intervention status
variable. This will be based on `CAD_history`, `Stroke_history`, and
`Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)
table(AEDB$Stroke_history)
table(AEDB$Peripheral.interv)
table(AEDB$MedHx_CVD)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix and inverse-rank normal transform the continuous (manually)
scored plaque phenotypes.

```{r IRNT PlaquePhenotypes}
AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

AEDB$MAC_rankNorm <- qnorm((rank(AEDB$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$macmean0)))
AEDB$SMC_rankNorm <- qnorm((rank(AEDB$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$smcmean0)))
AEDB$Neutrophils_rankNorm <- qnorm((rank(AEDB$neutrophils, na.last = "keep") - 0.5) / sum(!is.na(AEDB$neutrophils)))
AEDB$MastCells_rankNorm <- qnorm((rank(AEDB$Mast_cells_plaque, na.last = "keep") - 0.5) / sum(!is.na(AEDB$Mast_cells_plaque)))
AEDB$VesselDensity_rankNorm <- qnorm((rank(AEDB$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB$vessel_density_averaged)))

```

```{r IRNT PlaquePhenotypes: Visualisation}
library(labelled)
AEDB$Gender <- to_factor(AEDB$Gender)
ggpubr::gghistogram(AEDB, "macmean0", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% of macrophages (CD68)",
                    xlab = "% per region of interest", 
                    ggtheme = theme_minimal())

ggpubr::gghistogram(AEDB, "MAC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% of macrophages (CD68)",
                   xlab = "% per region of interest\ninverse-rank normalized number", 
                    ggtheme = theme_minimal())

ggpubr::gghistogram(AEDB, "smcmean0", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% of smooth muscle cells (SMA)",
                    xlab = "% per region of interest", 
                    ggtheme = theme_minimal())

ggpubr::gghistogram(AEDB, "SMC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% of smooth muscle cells (SMA)",
                   xlab = "% per region of interest\ninverse-rank normalized number", 
                    ggtheme = theme_minimal())

ggpubr::gghistogram(AEDB, "neutrophils", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of neutrophils (CD66b)",
                    xlab = "counts per plaque", 
                    ggtheme = theme_minimal())

ggpubr::gghistogram(AEDB, "Neutrophils_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of neutrophils (CD66b)",
                   xlab = "counts per plaque\ninverse-rank normalized number", 
                    ggtheme = theme_minimal())

ggpubr::gghistogram(AEDB, "Mast_cells_plaque", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of mast cells",
                    xlab = "counts per plaque", 
                    ggtheme = theme_minimal())

ggpubr::gghistogram(AEDB, "MastCells_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of mast cells",
                   xlab = "counts per plaque\ninverse-rank normalized number", 
                    ggtheme = theme_minimal())

ggpubr::gghistogram(AEDB, "vessel_density_averaged", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels",
                    xlab = "counts per 3-4 hotspots", 
                    ggtheme = theme_minimal())

ggpubr::gghistogram(AEDB, "VesselDensity_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels",
                   xlab = "counts per 3-4 hotspots\ninverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

Here we calculate the *plaque instability/vulnerability* index

```{r Plaque Vulnerability}
# Plaque vulnerability
require(labelled)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)

table(AEDB$Macrophages.bin)
table(AEDB$Fat.bin_10)
table(AEDB$Collagen.bin)
table(AEDB$SMC.bin)
table(AEDB$IPH.bin)

# SPSS code

# 
# *** syntax- Plaque vulnerability**.
# COMPUTE Macro_instab = -999.
# IF macrophages.bin=2 Macro_instab=1.
# IF macrophages.bin=1 Macro_instab=0.
# EXECUTE.
# 
# COMPUTE Fat10_instab = -999.
# IF Fat.bin_10=2 Fat10_instab=1.
# IF Fat.bin_10=1 Fat10_instab=0.
# EXECUTE.
# 
# COMPUTE coll_instab=-999.
# IF Collagen.bin=2 coll_instab=0.
# IF Collagen.bin=1 coll_instab=1.
# EXECUTE.
# 
# 
# COMPUTE SMC_instab=-999.
# IF SMC.bin=2 SMC_instab=0.
# IF SMC.bin=1 SMC_instab=1.
# EXECUTE.
# 
# COMPUTE IPH_instab=-999.
# IF IPH.bin=0 IPH_instab=0.
# IF IPH.bin=1 IPH_instab=1.
# EXECUTE.
# 
# COMPUTE Instability=Macro_instab + Fat10_instab +  coll_instab + SMC_instab + IPH_instab.
# EXECUTE.

# Fix plaquephenotypes
attach(AEDB)
# mac instability
AEDB[,"MAC_Instability"] <- NA
AEDB$MAC_Instability[Macrophages.bin == -999] <- NA
AEDB$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
AEDB$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1

# fat instability
AEDB[,"FAT10_Instability"] <- NA
AEDB$FAT10_Instability[Fat.bin_10 == -999] <- NA
AEDB$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
AEDB$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1

# col instability 
AEDB[,"COL_Instability"] <- NA
AEDB$COL_Instability[Collagen.bin == -999] <- NA
AEDB$COL_Instability[Collagen.bin == "no/minor"] <- 1
AEDB$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0

# smc instability
AEDB[,"SMC_Instability"] <- NA
AEDB$SMC_Instability[SMC.bin == -999] <- NA
AEDB$SMC_Instability[SMC.bin == "no/minor"] <- 1
AEDB$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0

# iph instability
AEDB[,"IPH_Instability"] <- NA
AEDB$IPH_Instability[IPH.bin == -999] <- NA
AEDB$IPH_Instability[IPH.bin == "no"] <- 0
AEDB$IPH_Instability[IPH.bin == "yes"] <- 1

detach(AEDB)

table(AEDB$MAC_Instability, useNA = "ifany")
table(AEDB$FAT10_Instability, useNA = "ifany")
table(AEDB$COL_Instability, useNA = "ifany")
table(AEDB$SMC_Instability, useNA = "ifany")
table(AEDB$IPH_Instability, useNA = "ifany")

# creating vulnerability index
AEDB <- AEDB %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
                                )

table(AEDB$Plaque_Vulnerability_Index, useNA = "ifany")

# str(AEDB$Plaque_Vulnerability_Index)

```

## Athero-Express Biobank Study

### Prepare baseline summary

We are interested in the following variables at baseline.

-   Age (years)

-   Female sex (N, %)

-   Hypertension (N, %)

-   SBP (mmHg)

-   DBP (mmHg)

-   Diabetes mellitus (N, %)

-   Total cholesterol levels (mg/dL)

-   LDL cholesterol levels (mg/dL)

-   HDL cholesterol levels (mg/dL)

-   Triglyceride levels (mg/dL)

-   Use of statins (N, %)

-   Use of antiplatelet drugs (N, %)

-   BMI (kg/m²)

-   Smoking status (N, %)

    -   Never smokers
    -   Ex-smokers
    -   Current smokers

-   History of CAD (N, %)

-   History of PAD (N, %)

-   Clinical manifestations

    -   Asymptomatic
    -   Amaurosis fugax
    -   TIA
    -   Stroke

-   eGFR (mL/min/1.73 m²)

-   stenosis

-   year of surgery

-   plaque characteristics

```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
# table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
AEDB$MedHx_CVD <- to_factor(AEDB$MedHx_CVD)


AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd")
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)

AEDB.full <- subset(AEDB,
                    informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd")
# AEDB.CEA[1:10, 1:10]
dim(AEDB.full)

```

```{r}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", 
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "SMC_rankNorm", "MAC_rankNorm", "Neutrophils_rankNorm", "MastCells_rankNorm", "VesselDensity_rankNorm")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", 
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

### All patients

Showing the baseline table of the whole Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.full, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients

```{r Baseline AEDB: Visualize AEDB CEA}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

## Athero-Express Single-Cell RNA Study (AESCRNA)

### Baseline summary

```{r Baseline: creation}
metadata <- scRNAseqData@meta.data %>% as_tibble()
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)

scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)

# Replace missing data 
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)

na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A", 
                "Not Available", "Not available", 
                "missing", 
                "-999", "-99", 
                "No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.

scRNAseqDataMetaAE %>%
  replace_with_na_all(condition = ~.x %in% na_strings)
```


```{r Baseline: selection}
cat("====================================================================================================")
cat("SELECTION THE SHIZZLE")

cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")

ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")

ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany")

rm(ae.gender, ae.hospital, ae.artery, ae.ic)


scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
                            (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
                              informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                              informedconsent != "no, died" &
                              informedconsent != "yes, no tissue, no commerical business" &
                              informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                              informedconsent != "yes, no tissue, no health treatment" &
                              informedconsent != "yes, no tissue, no questionnaires" &
                              informedconsent != "yes, no tissue, health treatment when possible" &
                              informedconsent != "yes, no tissue" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                              informedconsent != "no, doesn't want to" &
                              informedconsent != "no, unable to sign" &
                              informedconsent != "no, no reaction" &
                              informedconsent != "no, lost" &
                              informedconsent != "no, too old" &
                              informedconsent != "yes, no medical info, health treatment when possible" & 
                              informedconsent != "no (never asked for IC because there was no tissue)" &
                              informedconsent != "no, endpoint" &
                              informedconsent != "nooit geincludeerd")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)
# DT::datatable(scRNAseqDataMetaAE.all)

```

Showing the baseline table.

```{r Baseline: Visualize}
cat("===========================================================================================")
cat("CREATE BASELINE TABLE")

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  # strata = "Gender",
                                                  data = scRNAseqDataMetaAE.all, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]

```

### Saving baseline for AESCRNA

Writing the baseline table to Excel format.

```{r Baseline: write}
# Write basetable
require(openxlsx)
write.xlsx(file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.scRNAseq.xlsx"),
           format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE), 
           rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

```

# AESCRNA

## Quality control

Here review the number of cells per sample, plate, and patients. And plot the
ratio's per sample and study number.

```{r QualityControl}
## check stuff
cat("\nHow many cells per type ...?")
sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))

cat("\n\nHow many cells per plate ...?")
sort(table(scRNAseqData@meta.data$ID))

cat("\n\nHow many cells per type per plate ...?")
table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)

cat("\n\nHow many cells per patient ...?")
sort(table(scRNAseqData@meta.data$Patient))

cat("\n\nVisualizing these ratio's per study number and sample ...?")
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())


barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)), 
        cex.axis = 1.0, cex.names = 0.5, las = 1,
        col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
dev.off()

barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)), 
        cex.axis = 1.0, cex.names = 0.5, las = 2,
        col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
dev.off()



```

## Visualisations

Let's project known cellular markers.

```{r Visualisation: tSNE Exploration}

UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDN1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("ACKR1"), cols =  c("#ECECEC", "#DB003F"))

# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols =  c("#ECECEC", "#DB003F"))

# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD36"), cols =  c("#ECECEC", "#DB003F"))

# t-cells
FeaturePlot(scRNAseqData, features = c("CD3E"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD4"), cols =  c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqData, features = c("CD8"), cols =  c("#ECECEC", "#DB003F"))

# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols =  c("#ECECEC", "#DB003F"))

# mast cells
FeaturePlot(scRNAseqData, features = c("KIT"), cols =  c("#ECECEC", "#DB003F"))

# NK cells
FeaturePlot(scRNAseqData, features = c("NCAM1"), cols =  c("#ECECEC", "#DB003F"))

```

## Targets of interest

We check whether the targets genes, *`r target_genes`*, were sequenced using our
method (STARseq).

Several genes are not present or have different names, these are listed here,
and were manually removed from/changed in the list.

-   COL3A, not found
-   COL2A, not found

### Expression in cell communities

```{r Visualisation: Targets}
target_genes_rm <- c("COL3A", "COL2A")

temp = target_genes[!target_genes %in% target_genes_rm]

target_genes_qc <- c(temp)

# VlnPlot(scRNAseqData, features = "LINC01600")

# Make directory for plots
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlots")), 
       dir.create(file.path(PLOT_loc, "/VlnPlots")), 
       FALSE)
VLN_loc = paste0(PLOT_loc,"/VlnPlots")

# Make directory for plots
ifelse(!dir.exists(file.path(PLOT_loc, "/DotPlots")), 
       dir.create(file.path(PLOT_loc, "/DotPlots")), 
       FALSE)
DOT_loc = paste0(PLOT_loc,"/DotPlots")

# Make directory for plots
ifelse(!dir.exists(file.path(PLOT_loc, "/FeaturePlots")), 
       dir.create(file.path(PLOT_loc, "/FeaturePlots")), 
       FALSE)
FEAT_loc = paste0(PLOT_loc,"/FeaturePlots")


for (GENE in target_genes_qc){
  print(paste0("Projecting the expression of ", GENE, "."))

  vp1 <-  VlnPlot(scRNAseqData, features = GENE) + 
    xlab("cell communities") + 
    ylab(bquote("normalized expression")) +
    theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"), 
            axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
            legend.position = "none")
    ggsave(paste0(VLN_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
    ggsave(paste0(VLN_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
  
  print(vp1)
  
}


library(RColorBrewer)

p1 <- DotPlot(scRNAseqData, features = target_genes_qc,
        cols = "RdBu")

p1 + theme(axis.text.x = element_text(angle = 45, hjust=1, size = 5))

ggsave(paste0(DOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())
ggsave(paste0(DOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())

rm(p1)

FeaturePlot(scRNAseqData, features = c(target_genes_qc),
            cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            combine = TRUE)

ggsave(paste0(FEAT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
ggsave(paste0(FEAT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())

for (GENE in target_genes_qc){
  print(paste0("Projecting the expression of ", GENE, "."))

  fp1 <-  FeaturePlot(scRNAseqData, features = GENE, cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            combine = TRUE) + 
    xlab("cell communities") + 
    ylab(bquote("normalized expression")) +
    theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"), 
            axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
            legend.position = "right")
    ggsave(paste0(FEAT_loc, "/", Today, ".FeaturePlot.",GENE,".png"), plot = last_plot())
    ggsave(paste0(FEAT_loc, "/", Today, ".FeaturePlot.",GENE,".ps"), plot = last_plot())
  
  print(fp1)
  
}

```

### Differential expression between cell communities

Here we project genes to only the broad cell communities:

-   macrophages
-   endothelial cells
-   smooth muscle cells
-   T-cells
-   B-cells
-   Mast cells
-   NK-cells
-   Mixed cells

#### Macrophages

Comparison between the macrophages cell communities (*CD14/CD68*<sup>+</sup>),
and all other communities.

```{r Visualisation: Volcano MAC}

MAC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III"), 
                          ident.2 = c(#"CD14+CD68+ M I", 
                                      #"CD14+CD68+ M II", 
                                      #"CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC", 
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

DT::datatable(MAC.markers)

MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
    lab = rownames(MAC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Macrophage markers\n(Macrophage communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
# MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"), 
       plot = MAC_Volcano_TargetsA)

```

The target results are given below and written to a file.

```{r Results MAC}
library(tibble)
MAC.markers <- add_column(MAC.markers, Gene = row.names(MAC.markers), .before = 1)

temp <- MAC.markers[MAC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MAC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MAC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Smooth muscle cells

Comparison between the smooth muscle cell communities (*ACTA2*<sup>+</sup>), and
all other communities.

```{r Visualisation: Volcano SMC}

SMC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("ACTA2+ SMC"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      # "ACTA2+ SMC", 
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

DT::datatable(SMC.markers)

SMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
    lab = rownames(SMC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "SMC markers\n(SMC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
# SMC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"), 
       plot = SMC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results SMC}
library(tibble)
SMC.markers <- add_column(SMC.markers, Gene = row.names(SMC.markers), .before = 1)

temp <- SMC.markers[SMC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results SMC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".SMC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Endothelial cells

Comparison between the endothelial cell communities (*CD34*<sup>+</sup>), and
all other communities.

```{r Visualisation: Volcano EC}

EC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD34+ EC I", "CD34+ EC II"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs",
                                      # "CD34+ EC I", 
                                      # "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC",
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

DT::datatable(EC.markers)

EC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
    lab = rownames(EC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Endothelial cell markers\n(EC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
# EC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"), 
       plot = EC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results EC}
library(tibble)
EC.markers <- add_column(EC.markers, Gene = row.names(EC.markers), .before = 1)

temp <- EC.markers[EC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results EC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".EC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### T-cells

Comparison between the T-cell communities (*CD3/CD4/CD8*<sup>+</sup>), and all
other communities.

```{r Visualisation: Volcano Tcell}

TC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      # "CD3+CD8+ T I",
                                      # "CD3+CD8A+ T II ", 
                                      # "CD3+CD8A+ T III", 
                                      # "CD3+CD4+ T I", 
                                      # "CD3+CD4+ T II", 
                                      # "CD3 Tregs", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC",
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

DT::datatable(TC.markers)

TC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
    lab = rownames(TC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "T-cell markers\n(T-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
# TC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"), 
       plot = TC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results TC}
library(tibble)
TC.markers <- add_column(TC.markers, Gene = row.names(TC.markers), .before = 1)

temp <- TC.markers[TC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results TC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".TC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### B-cells

Comparison between the B-cell communities (*CD79A*<sup>+</sup>), and all other
communities.

```{r Visualisation: Volcano Bcell}

BC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD79A+ B I", 
                                      "CD79A+ B II"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs",
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC",
                                      "NCAM1+ NK", 
                                      "KIT+ MC"))

DT::datatable(BC.markers)

BC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
    lab = rownames(BC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "B-cell markers\n(B-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
# BC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"), 
       plot = BC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results BC}
library(tibble)
BC.markers <- add_column(BC.markers, Gene = row.names(BC.markers), .before = 1)

temp <- BC.markers[BC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results BC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".BC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Mast cells

Comparison between the mast cell communities (*KIT*<sup>+</sup>), and all other
communities.

```{r Visualisation: Volcano Mast}

MC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("KIT+ MC"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs",
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC",
                                      "NCAM1+ NK", 
                                      # "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

DT::datatable(MC.markers)

MC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
    lab = rownames(MC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Mast cell markers\n(Mast cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
# MC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"), 
       plot = MC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results MC}
library(tibble)
MC.markers <- add_column(MC.markers, Gene = row.names(MC.markers), .before = 1)

temp <- MC.markers[MC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### NK-cells

Comparison between the natural killer cell communities (*NCAM1*<sup>+</sup>),
and all other communities.

```{r Visualisation: Volcano NK}

NK.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("NCAM1+ NK"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs",
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "Mixed I",
                                      "Mixed II",
                                      "ACTA2+ SMC", 
                                      # "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

DT::datatable(NK.markers)

NK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
    lab = rownames(NK.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "NK markers\n(NK-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
# NK_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"), 
       plot = NK_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results NK}
library(tibble)
NK.markers <- add_column(NK.markers, Gene = row.names(NK.markers), .before = 1)

temp <- NK.markers[NK.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results NK: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".NK.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Mixed cells

Comparison between the mixed cell communities, and all other communities.

```{r Visualisation: Volcano MIXED}

MIXED.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("Mixed I", 
                                      "Mixed II"), 
                          ident.2 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II", 
                                      "CD3+CD8A+ T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3 Tregs",
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      # "Mixed I", 
                                      # "Mixed II", 
                                      "ACTA2+ SMC", 
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

DT::datatable(MIXED.markers)

MIXED_Volcano_TargetsA = EnhancedVolcano(MIXED.markers,
    lab = rownames(MIXED.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Mixed markers\n(Mixed cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels=c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
# MIXED_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MIXED.DEG.Targets.pdf"), 
       plot = MIXED_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results MIXED}
library(tibble)
MIXED.markers <- add_column(MIXED.markers, Gene = row.names(MIXED.markers), .before = 1)

temp <- MIXED.markers[MIXED.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MIXED: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MIXED.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

# Session information

--------------------------------------------------------------------------------

    Version:      v1.1.1
    Last update:  2021-10-29
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

    Change log
    * v1.1.1 Update on the AEDB.
    * v1.1.0 Major overhaul; update to WORCS system. Also including multiple options for scRNAseq datasets.
    * v1.0.4 Small bug fixes.
    * v1.0.3 Fixed weight further by excluding some graphs from the Rmd - obviously these can be added with sharing with third parties, but these are too heavy for a template.
    * v1.0.2 Fixed weight of files (limit of 10Mb per file for templates). 
    * v1.0.1 Updated background information.
    * v1.0.0 Initial version.

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment

```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".scrnaseq_results.RData"))
```

|                                                                                                                                                 |
|-------------------------------------------------------------------------------------------------------------------------------------------------|
| <sup>© 1979-2021 Sander W. van der Laan \| s.w.vanderlaan-2[at]umcutrecht.nl \| [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup> |
